biosc - diagnostics: isochrones and $A(Li)$ model comparison¶
string = 'work'
## to change directory:
## string_directory = 'path...'
from matplotlib.path import Path
import pymc as pm
import arviz as az
import bambi as bmb
import xarray as xr
import biosc
import biosc.preprocessing
import matplotlib.ticker as ticker
from pymc import HalfCauchy, Model, Normal, sample
import os
import matplotlib.cm as cm
from netCDF4 import Dataset as NetCDFFile
from scipy.stats import gaussian_kde
from biosc.preprocessing import Preprocessing
from biosc.bhm import BayesianModel
import models_test
Jmag_lbda = 12350.00
Hmag_lbda = 16620.00
Kmag_lbda = 21590.00
BP_lbda = 5109.71
G_lbda = 6217.59
RP_lbda = 7769.02
gmag_lbda = 4849.11
rmag_lbda = 6201.20
wmag_lbda = 6285.91
imag_lbda = 7534.96
zmag_lbda = 8674.20
ymag_lbda = 9627.79
import models_test
from models_test import plt, np, pd, pc, select_nearest_age
path_all = pc(string)
## to change directory:
## path_all = pc_other(string_directory)
import sys
sys.path.append(path_all)
import bmp
from bmp import BayesianModelPlots
path_all
'/pcdisk/dalen/lgonzalez/OneDrive/Escritorio/CAB_INTA-CSIC/01-Doctorado/013-Jupyter/0134-biosc_env/'
plt.rcParams.update({'font.size': 14, 'axes.linewidth': 1, 'axes.edgecolor': 'k'})
plt.rcParams['font.family'] = 'serif'
Pleiades data¶
from models_test import PleiadesData
path_data = path_all + 'data/Pleiades_DANCe+GDR3+2MASS+PanSTARRS1+A_Li+Lbol.csv'
pleiades_data = PleiadesData(path_data)
data_obs_Pleiades = pleiades_data.data
data_obs_Pleiades
| source_id | Mecayotl | Olivares+2018 | Meingast+2021 | l | b | ra | ra_error | dec | dec_error | ... | r_abs | i_abs | y_abs | z_abs | g_abs | G-J | G-RP | BP-RP | Lsun | log(L/Lsun) | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 66787119410915072 | True | True | True | 166.210733 | -23.276099 | 56.662143 | 0.014997 | 24.520108 | 0.009073 | ... | 7.418857 | 6.972857 | 6.525157 | 6.694857 | 8.224757 | 1.826829 | 0.810348 | 1.512766 | 0.155611 | -0.807959 |
| 1 | 64977705525131904 | True | True | True | 167.014088 | -24.105530 | 56.647305 | 0.018675 | 23.411531 | 0.012227 | ... | 8.549097 | 7.790097 | 7.159997 | 7.424497 | 9.772697 | 2.421177 | 1.006676 | 2.033531 | 0.076629 | -1.115607 |
| 2 | 65195404530870144 | True | True | True | 166.401835 | -23.959280 | 56.302660 | 0.187139 | 23.895691 | 0.137836 | ... | 14.008637 | 12.001337 | 10.590737 | 11.072637 | 15.369937 | 3.461519 | 1.369004 | 3.289309 | NaN | NaN |
| 3 | 64942001460286080 | True | True | True | 167.530351 | -23.713150 | 57.315181 | 0.117257 | 23.380170 | 0.079404 | ... | 13.164333 | 11.330633 | 10.050533 | 10.483733 | 14.439433 | 3.319764 | 1.337366 | 3.338682 | 0.007924 | -2.101050 |
| 4 | 64433924008996224 | True | True | True | 167.187075 | -25.469587 | 55.777180 | 0.158969 | 22.296886 | 0.108396 | ... | 13.654355 | 11.738355 | 10.399955 | 10.850355 | 15.065555 | 3.404532 | 1.361330 | 3.481064 | 0.006176 | -2.209322 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 941 | 68045583484082048 | True | True | True | 164.243788 | -25.259933 | 53.747213 | 0.023608 | 24.204450 | 0.018528 | ... | 9.355493 | 8.326793 | 7.636193 | 7.872993 | 10.560393 | 2.526645 | 1.083299 | 2.277171 | 0.052494 | -1.279886 |
| 942 | 65113559634339200 | True | True | True | 165.803768 | -25.252330 | 54.921665 | 0.014164 | 23.290687 | 0.010935 | ... | 4.161775 | 4.078775 | 4.069775 | 4.064775 | 4.429775 | 0.890571 | 0.437847 | 0.711143 | 1.683682 | 0.226260 |
| 943 | 64987876007763968 | True | True | True | 167.304764 | -23.830079 | 57.063558 | 0.061383 | 23.434686 | 0.049673 | ... | 11.529959 | 10.045959 | 9.017459 | 9.351059 | 12.720659 | 3.020585 | 1.246986 | 2.865516 | 0.015930 | -1.797787 |
| 944 | 71056252479453056 | True | True | True | 163.033014 | -22.724805 | 54.616063 | 0.095418 | 26.856932 | 0.065394 | ... | 12.175861 | 10.479061 | 9.307561 | 9.688561 | 13.449461 | 3.192930 | 1.292702 | 3.159868 | 0.011978 | -1.921621 |
| 945 | 65129506848731136 | True | True | True | 166.221501 | -24.634561 | 55.676696 | 0.013654 | 23.502637 | 0.009224 | ... | 6.892914 | 6.468914 | 6.072914 | 6.199914 | 7.756914 | 1.825894 | 0.746666 | 1.346799 | 0.238297 | -0.622882 |
946 rows × 100 columns
data_obs_Pleiades.columns
Index(['source_id', 'Mecayotl', 'Olivares+2018', 'Meingast+2021', 'l', 'b',
'ra', 'ra_error', 'dec', 'dec_error', 'parallax', 'parallax_error',
'pmra', 'pmra_error', 'pmdec', 'pmdec_error', 'pmra_pmdec_corr',
'ra_dec_corr', 'ra_parallax_corr', 'ra_pmra_corr', 'ra_pmdec_corr',
'dec_parallax_corr', 'dec_pmra_corr', 'dec_pmdec_corr',
'parallax_pmra_corr', 'parallax_pmdec_corr', 'g', 'bp', 'rp', 'e_g',
'e_bp', 'e_rp', 'dr3_radial_velocity', 'dr3_radial_velocity_error',
'ruwe', 'astrometric_excess_noise', 'astrometric_params_solved',
'bp_rp', 'g_rp', 'Jmag', 'Hmag', 'Kmag', 'e_Jmag', 'e_Hmag', 'e_Kmag',
'gmag', 'e_gmag', 'rmag', 'e_rmag', 'imag', 'e_imag', 'zmag', 'e_zmag',
'ymag', 'e_ymag', 'Name', 'EPIC', 'RAJ2000', 'DEJ2000', 'Vmag', 'J-K',
'Per', 'Amp', 'l_WLi', 'WLi', 'e_WLi', 'Teff', 'ALi', 'e_ALi', 'Bin',
'SimbadName', 'Teff_x', 'logg', '[Fe/H]', 'A0', 'AG', 'ABP', 'ARP',
'E(BP-RP)', 'Rad', 'Lib', 'angDist', 'distance', 'distance_modulus',
'G_abs', 'BP_abs', 'RP_abs', 'J_abs', 'H_abs', 'K_abs', 'r_abs',
'i_abs', 'y_abs', 'z_abs', 'g_abs', 'G-J', 'G-RP', 'BP-RP', 'Lsun',
'log(L/Lsun)'],
dtype='object')
data_obs_Pleiades['e_G'] = data_obs_Pleiades['e_g']
data_obs_Pleiades['e_RP'] = data_obs_Pleiades['e_rp']
data_obs_Pleiades['e_BP'] = data_obs_Pleiades['e_bp']
data_obs_Pleiades['e_J'] = data_obs_Pleiades['e_Jmag']
data_obs_Pleiades['e_K'] = data_obs_Pleiades['e_Kmag']
data_obs_Pleiades['e_H'] = data_obs_Pleiades['e_Hmag']
data_obs_Pleiades['e_r'] = data_obs_Pleiades['e_rmag']
data_obs_Pleiades['e_i'] = data_obs_Pleiades['e_imag']
data_obs_Pleiades['e_z'] = data_obs_Pleiades['e_zmag']
data_obs_Pleiades['e_y'] = data_obs_Pleiades['e_ymag']
data_obs_Pleiades['e_gmag'] = data_obs_Pleiades['e_gmag']
data_obs_Pleiades['Teff_x']
0 4410.5390
1 3664.9885
2 NaN
3 3131.1300
4 3092.2827
...
941 3602.4023
942 6055.7964
943 3315.6130
944 3214.3660
945 4741.1820
Name: Teff_x, Length: 946, dtype: float64
np.mean(data_obs_Pleiades['ALi'])
2.227078431372549
#sigma = 5.67e-8 # W/m²/K⁴
solar_abundance = 1.05
e_solar_abundance = 0.10
Zsun = 0.01524
#Asplund et al. 2009
Models¶
from models_test import Models
models = Models()
PARSEC¶
parsec = models.PARSEC(path_all)
PARSEC_00 = parsec.get_dataframe()
PARSEC_00
| Zini | MH | logAge | Mini | int_IMF | M/Ms | logL | logTe | logg | label | ... | wP1_i45 | wP1_i50 | wP1_i55 | wP1_i60 | wP1_i65 | wP1_i70 | wP1_i75 | wP1_i80 | wP1_i85 | wP1_i90 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 0.01471 | 0.0 | 6.30103 | 0.090000 | 1.081690 | 0.090 | -1.379 | 3.4384 | 3.477 | 0.0 | ... | 10.378 | 10.378 | 10.378 | 10.378 | 10.378 | 10.378 | 10.378 | 10.378 | 10.378 | 10.378 |
| 1 | 0.01471 | 0.0 | 6.30103 | 0.097813 | 1.152571 | 0.098 | -1.307 | 3.4443 | 3.465 | 0.0 | ... | 10.112 | 10.112 | 10.112 | 10.112 | 10.112 | 10.112 | 10.112 | 10.112 | 10.112 | 10.112 |
| 2 | 0.01471 | 0.0 | 6.30103 | 0.100000 | 1.171106 | 0.100 | -1.288 | 3.4459 | 3.462 | 0.0 | ... | 10.042 | 10.042 | 10.042 | 10.042 | 10.042 | 10.042 | 10.042 | 10.042 | 10.042 | 10.042 |
| 3 | 0.01471 | 0.0 | 6.30103 | 0.100258 | 1.173256 | 0.100 | -1.286 | 3.4461 | 3.461 | 0.0 | ... | 10.035 | 10.035 | 10.035 | 10.035 | 10.035 | 10.035 | 10.035 | 10.035 | 10.035 | 10.035 |
| 4 | 0.01471 | 0.0 | 6.30103 | 0.109296 | 1.244379 | 0.109 | -1.224 | 3.4508 | 3.455 | 0.0 | ... | 9.811 | 9.811 | 9.811 | 9.811 | 9.811 | 9.811 | 9.811 | 9.811 | 9.811 | 9.811 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 41620 | 0.01471 | 0.0 | 8.77597 | 2.692088 | 2.589498 | 2.690 | 2.284 | 3.6668 | 2.203 | 7.0 | ... | -0.810 | -0.810 | -0.810 | -0.810 | -0.810 | -0.810 | -0.810 | -0.810 | -0.810 | -0.810 |
| 41621 | 0.01471 | 0.0 | 8.77597 | 2.692170 | 2.589500 | 2.690 | 2.276 | 3.6678 | 2.215 | 7.0 | ... | -0.793 | -0.793 | -0.793 | -0.793 | -0.793 | -0.793 | -0.793 | -0.793 | -0.793 | -0.793 |
| 41622 | 0.01471 | 0.0 | 8.77597 | 2.692199 | 2.589501 | 2.690 | 2.250 | 3.6700 | 2.249 | 7.0 | ... | -0.737 | -0.737 | -0.737 | -0.737 | -0.737 | -0.737 | -0.737 | -0.737 | -0.737 | -0.737 |
| 41623 | 0.01471 | 0.0 | 8.77597 | 2.692242 | 2.589502 | 2.690 | 2.217 | 3.6727 | 2.294 | 7.0 | ... | -0.663 | -0.663 | -0.663 | -0.663 | -0.663 | -0.663 | -0.663 | -0.663 | -0.663 | -0.663 |
| 41624 | 0.01471 | 0.0 | 8.77597 | 2.692394 | 2.589505 | 2.690 | 2.213 | 3.6727 | 2.297 | 7.0 | ... | -0.653 | -0.653 | -0.653 | -0.653 | -0.653 | -0.653 | -0.653 | -0.653 | -0.653 | -0.653 |
41625 rows × 312 columns
PARSEC_00_sun = parsec.get_dataframe(sun=True)
PARSEC_00_sun
| Zini | MH | logAge | Mini | int_IMF | M/Ms | logL | logTe | logg | label | ... | wP1_i45 | wP1_i50 | wP1_i55 | wP1_i60 | wP1_i65 | wP1_i70 | wP1_i75 | wP1_i80 | wP1_i85 | wP1_i90 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 0.00713 | -0.3272 | 6.30103 | 0.090000 | 1.081690 | 0.090 | -1.270 | 3.4655 | 3.477 | 0.0 | ... | 9.549 | 9.549 | 9.549 | 9.549 | 9.549 | 9.549 | 9.549 | 9.549 | 9.549 | 9.549 |
| 1 | 0.00713 | -0.3272 | 6.30103 | 0.096622 | 1.142251 | 0.097 | -1.208 | 3.4700 | 3.463 | 0.0 | ... | 9.346 | 9.346 | 9.346 | 9.346 | 9.346 | 9.346 | 9.346 | 9.346 | 9.346 | 9.346 |
| 2 | 0.00713 | -0.3272 | 6.30103 | 0.099697 | 1.168569 | 0.100 | -1.182 | 3.4720 | 3.458 | 0.0 | ... | 9.261 | 9.261 | 9.261 | 9.261 | 9.261 | 9.261 | 9.261 | 9.261 | 9.261 | 9.261 |
| 3 | 0.00713 | -0.3272 | 6.30103 | 0.108216 | 1.236287 | 0.108 | -1.115 | 3.4768 | 3.446 | 0.0 | ... | 9.047 | 9.047 | 9.047 | 9.047 | 9.047 | 9.047 | 9.047 | 9.047 | 9.047 | 9.047 |
| 4 | 0.00713 | -0.3272 | 6.30103 | 0.113255 | 1.273161 | 0.113 | -1.081 | 3.4794 | 3.442 | 0.0 | ... | 8.936 | 8.936 | 8.936 | 8.936 | 8.936 | 8.936 | 8.936 | 8.936 | 8.936 | 8.936 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 37339 | 0.00713 | -0.3272 | 8.77597 | 2.546733 | 2.586173 | 2.546 | 2.300 | 3.6824 | 2.226 | 7.0 | ... | -0.889 | -0.889 | -0.889 | -0.889 | -0.889 | -0.889 | -0.889 | -0.889 | -0.889 | -0.889 |
| 37340 | 0.00713 | -0.3272 | 8.77597 | 2.546813 | 2.586175 | 2.546 | 2.297 | 3.6829 | 2.230 | 7.0 | ... | -0.886 | -0.886 | -0.886 | -0.886 | -0.886 | -0.886 | -0.886 | -0.886 | -0.886 | -0.886 |
| 37341 | 0.00713 | -0.3272 | 8.77597 | 2.546835 | 2.586176 | 2.546 | 2.279 | 3.6845 | 2.254 | 7.0 | ... | -0.845 | -0.845 | -0.845 | -0.845 | -0.845 | -0.845 | -0.845 | -0.845 | -0.845 | -0.845 |
| 37342 | 0.00713 | -0.3272 | 8.77597 | 2.546864 | 2.586176 | 2.546 | 2.255 | 3.6866 | 2.287 | 7.0 | ... | -0.789 | -0.789 | -0.789 | -0.789 | -0.789 | -0.789 | -0.789 | -0.789 | -0.789 | -0.789 |
| 37343 | 0.00713 | -0.3272 | 8.77597 | 2.547009 | 2.586180 | 2.546 | 2.235 | 3.6877 | 2.312 | 7.0 | ... | -0.743 | -0.743 | -0.743 | -0.743 | -0.743 | -0.743 | -0.743 | -0.743 | -0.743 | -0.743 |
37344 rows × 312 columns
age = 0.120
PARSEC_00_120 = parsec.get_dataframe_by_age(age)
PARSEC_00_120
| Zini | MH | logAge | Mini | int_IMF | M/Ms | logL | logTe | logg | label | ... | wP1_i55 | wP1_i60 | wP1_i65 | wP1_i70 | wP1_i75 | wP1_i80 | wP1_i85 | wP1_i90 | Teff | Lsun | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 0.01471 | 0.0 | 8.08636 | 0.090000 | 1.081690 | 0.090 | -2.818 | 3.4010 | 4.766 | 0.0 | ... | 14.470 | 14.470 | 14.470 | 14.470 | 14.470 | 14.470 | 14.470 | 14.470 | 2517.676928 | 0.001521 |
| 1 | 0.01471 | 0.0 | 8.08636 | 0.091255 | 1.093600 | 0.091 | -2.808 | 3.4024 | 4.768 | 0.0 | ... | 14.445 | 14.445 | 14.445 | 14.445 | 14.445 | 14.445 | 14.445 | 14.445 | 2525.806055 | 0.001556 |
| 2 | 0.01471 | 0.0 | 8.08636 | 0.098871 | 1.161602 | 0.099 | -2.744 | 3.4117 | 4.776 | 0.0 | ... | 14.267 | 14.267 | 14.267 | 14.267 | 14.267 | 14.267 | 14.267 | 14.267 | 2580.477044 | 0.001803 |
| 3 | 0.01471 | 0.0 | 8.08636 | 0.100000 | 1.171106 | 0.100 | -2.735 | 3.4131 | 4.777 | 0.0 | ... | 14.229 | 14.229 | 14.229 | 14.229 | 14.229 | 14.229 | 14.229 | 14.229 | 2588.808942 | 0.001841 |
| 4 | 0.01471 | 0.0 | 8.08636 | 0.100865 | 1.178290 | 0.101 | -2.729 | 3.4140 | 4.778 | 0.0 | ... | 14.205 | 14.205 | 14.205 | 14.205 | 14.205 | 14.205 | 14.205 | 14.205 | 2594.179362 | 0.001866 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 120 | 0.01471 | 0.0 | 8.08636 | 2.699229 | 2.589651 | 2.699 | 1.794 | 4.0560 | 4.252 | 1.0 | ... | 0.913 | 0.913 | 0.913 | 0.913 | 0.913 | 0.913 | 0.913 | 0.913 | 11376.272858 | 62.230029 |
| 121 | 0.01471 | 0.0 | 8.08636 | 2.800000 | 2.591712 | 2.800 | 1.862 | 4.0656 | 4.238 | 1.0 | ... | 0.792 | 0.792 | 0.792 | 0.792 | 0.792 | 0.792 | 0.792 | 0.792 | 11630.543233 | 72.777980 |
| 122 | 0.01471 | 0.0 | 8.08636 | 3.000000 | 2.595334 | 3.000 | 1.992 | 4.0829 | 4.207 | 1.0 | ... | 0.560 | 0.560 | 0.560 | 0.560 | 0.560 | 0.560 | 0.560 | 0.560 | 12103.194151 | 98.174794 |
| 123 | 0.01471 | 0.0 | 8.08636 | 3.181551 | 2.598173 | 3.181 | 2.105 | 4.0971 | 4.176 | 1.0 | ... | 0.356 | 0.356 | 0.356 | 0.356 | 0.356 | 0.356 | 0.356 | 0.356 | 12505.469461 | 127.350308 |
| 124 | 0.01471 | 0.0 | 8.08636 | 3.200000 | 2.598441 | 3.199 | 2.116 | 4.0984 | 4.172 | 1.0 | ... | 0.334 | 0.334 | 0.334 | 0.334 | 0.334 | 0.334 | 0.334 | 0.334 | 12542.958923 | 130.617089 |
125 rows × 314 columns
PARSEC_00_120['Y']
0 0.2745
1 0.2745
2 0.2745
3 0.2745
4 0.2745
...
120 0.2746
121 0.2746
122 0.2746
123 0.2746
124 0.2746
Name: Y, Length: 125, dtype: float64
PARSEC_00_sun_120 = parsec.get_dataframe_by_age(age, sun=True)
PARSEC_00_sun_120
| Zini | MH | logAge | Mini | int_IMF | M/Ms | logL | logTe | logg | label | ... | wP1_i55 | wP1_i60 | wP1_i65 | wP1_i70 | wP1_i75 | wP1_i80 | wP1_i85 | wP1_i90 | Teff | Lsun | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 0.00713 | -0.3272 | 8.08636 | 0.090000 | 1.081690 | 0.090 | -2.769 | 3.4339 | 4.849 | 0.0 | ... | 13.741 | 13.741 | 13.741 | 13.741 | 13.741 | 13.741 | 13.741 | 13.741 | 2715.813858 | 0.001702 |
| 1 | 0.00713 | -0.3272 | 8.08636 | 0.090711 | 1.088470 | 0.091 | -2.764 | 3.4346 | 4.850 | 0.0 | ... | 13.717 | 13.717 | 13.717 | 13.717 | 13.717 | 13.717 | 13.717 | 13.717 | 2720.194762 | 0.001722 |
| 2 | 0.00713 | -0.3272 | 8.08636 | 0.096212 | 1.138659 | 0.096 | -2.721 | 3.4400 | 4.855 | 0.0 | ... | 13.526 | 13.526 | 13.526 | 13.526 | 13.526 | 13.526 | 13.526 | 13.526 | 2754.228703 | 0.001901 |
| 3 | 0.00713 | -0.3272 | 8.08636 | 0.099697 | 1.168569 | 0.100 | -2.694 | 3.4436 | 4.857 | 0.0 | ... | 13.404 | 13.404 | 13.404 | 13.404 | 13.404 | 13.404 | 13.404 | 13.404 | 2777.154236 | 0.002023 |
| 4 | 0.00713 | -0.3272 | 8.08636 | 0.105658 | 1.216707 | 0.106 | -2.653 | 3.4488 | 4.862 | 0.0 | ... | 13.224 | 13.224 | 13.224 | 13.224 | 13.224 | 13.224 | 13.224 | 13.224 | 2810.606200 | 0.002223 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 160 | 0.00713 | -0.3272 | 8.08636 | 3.026102 | 2.595767 | 3.025 | 2.093 | 4.1248 | 4.277 | 1.0 | ... | 0.568 | 0.568 | 0.568 | 0.568 | 0.568 | 0.568 | 0.568 | 0.568 | 13329.074642 | 123.879659 |
| 161 | 0.00713 | -0.3272 | 8.08636 | 3.171499 | 2.598026 | 3.170 | 2.183 | 4.1354 | 4.250 | 1.0 | ... | 0.405 | 0.405 | 0.405 | 0.405 | 0.405 | 0.405 | 0.405 | 0.405 | 13658.405431 | 152.405275 |
| 162 | 0.00713 | -0.3272 | 8.08636 | 3.181142 | 2.598167 | 3.180 | 2.189 | 4.1361 | 4.248 | 1.0 | ... | 0.394 | 0.394 | 0.394 | 0.394 | 0.394 | 0.394 | 0.394 | 0.394 | 13680.437931 | 154.525444 |
| 163 | 0.00713 | -0.3272 | 8.08636 | 3.362140 | 2.600650 | 3.360 | 2.297 | 4.1477 | 4.210 | 1.0 | ... | 0.191 | 0.191 | 0.191 | 0.191 | 0.191 | 0.191 | 0.191 | 0.191 | 14050.765963 | 198.152703 |
| 164 | 0.00713 | -0.3272 | 8.08636 | 3.368611 | 2.600733 | 3.366 | 2.301 | 4.1481 | 4.209 | 1.0 | ... | 0.184 | 0.184 | 0.184 | 0.184 | 0.184 | 0.184 | 0.184 | 0.184 | 14063.713158 | 199.986187 |
165 rows × 314 columns
PARSEC_00_sun_120['Y']
0 0.2615
1 0.2615
2 0.2615
3 0.2615
4 0.2615
...
160 0.2614
161 0.2614
162 0.2614
163 0.2614
164 0.2614
Name: Y, Length: 165, dtype: float64
PARSEC_iso_omega_00_Phot_dict, PARSEC_iso_omega_00_sun_Phot_dict = parsec._generate_dicts()
BT-Settl¶
path_models = path_all + 'data/BT-Settl_all_Myr_Gaia+2MASS+PanSTARRS.csv'
BTSettl_mod = models.BTSettl(path_models)
BTSettl = BTSettl_mod.get_dataframe()
BTSettl
| age_Gyr | t(Gyr) | M/Ms | Teff | log(L/Lsun) | lg(g) | R(Gcm) | D | Li | G_abs | ... | J_abs | H_abs | K_abs | g_abs | r_abs | i_abs | y_abs | z_abs | A(Li) | Lsun | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 0.001 | 0.001 | 0.010 | 2345.0 | -2.70 | 3.57 | 18.99 | 1.00 | 1.0000 | 14.055 | ... | 9.328 | 8.770 | 8.353 | 17.937 | 17.095 | 13.757 | 12.125 | 11.009 | 3.300000 | 0.001995 |
| 1 | 0.001 | 0.001 | 0.015 | 2504.0 | -2.42 | 3.58 | 22.90 | 1.00 | 1.0000 | 13.015 | ... | 8.667 | 8.135 | 7.751 | 16.291 | 15.787 | 12.540 | 11.155 | 10.214 | 3.300000 | 0.003802 |
| 2 | 0.001 | 0.001 | 0.020 | 2598.0 | -2.25 | 3.59 | 26.11 | 1.00 | 1.0000 | 12.381 | ... | 8.238 | 7.710 | 7.347 | 15.366 | 14.935 | 11.827 | 10.566 | 9.722 | 3.300000 | 0.005623 |
| 3 | 0.001 | 0.001 | 0.030 | 2710.0 | -1.98 | 3.57 | 32.78 | 1.00 | 1.0000 | 11.507 | ... | 7.582 | 7.054 | 6.720 | 14.194 | 13.747 | 10.885 | 9.754 | 9.006 | 3.300000 | 0.010471 |
| 4 | 0.001 | 0.001 | 0.040 | 2779.0 | -1.81 | 3.57 | 37.62 | 0.99 | 1.0000 | 10.990 | ... | 7.188 | 6.652 | 6.338 | 13.512 | 13.061 | 10.339 | 9.275 | 8.580 | 3.300000 | 0.015488 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 833 | 10.000 | 10.000 | 0.500 | 3689.0 | -1.43 | 4.78 | 33.05 | 0.00 | 0.0000 | 8.937 | ... | 6.589 | 5.944 | 5.721 | 10.162 | 9.297 | 8.342 | 7.936 | 7.713 | -inf | 0.037154 |
| 834 | 10.000 | 10.000 | 0.600 | 4013.0 | -1.12 | 4.70 | 39.92 | 0.00 | 0.0000 | 7.960 | ... | 5.926 | 5.237 | 5.059 | 9.010 | 8.100 | 7.463 | 7.188 | 7.015 | -inf | 0.075858 |
| 835 | 10.000 | 10.000 | 0.700 | 4493.0 | -0.79 | 4.63 | 46.43 | 0.00 | 0.0000 | 6.900 | ... | 5.244 | 4.614 | 4.490 | 7.754 | 6.870 | 6.537 | 6.392 | 6.271 | -inf | 0.162181 |
| 836 | 10.000 | 10.000 | 0.800 | 5002.0 | -0.47 | 4.55 | 54.44 | 0.00 | 0.0000 | 5.925 | ... | 4.580 | 4.098 | 4.000 | 6.529 | 5.885 | 5.675 | 5.594 | 5.525 | -inf | 0.338844 |
| 837 | 10.000 | 10.000 | 0.900 | 5495.0 | -0.12 | 4.42 | 67.60 | 0.00 | 0.0626 | 4.972 | ... | 3.860 | 3.490 | 3.410 | 5.422 | 4.944 | 4.804 | 4.769 | 4.741 | 2.096574 | 0.758578 |
838 rows × 22 columns
age = 0.120
BTSettl_120 = BTSettl_mod.get_dataframe_by_age(age)
BTSettl_120
| age_Gyr | t(Gyr) | M/Ms | Teff | log(L/Lsun) | lg(g) | R(Gcm) | D | Li | G_abs | ... | J_abs | H_abs | K_abs | g_abs | r_abs | i_abs | y_abs | z_abs | A(Li) | Lsun | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 488 | 0.12 | 0.12 | 0.020 | 1304.0 | -4.41 | 4.56 | 8.58 | 0.0 | 1.0000 | 19.367000 | ... | 14.167 | 12.877 | 12.291 | 25.102 | 22.184 | 20.307 | 17.194 | 16.000 | 3.300000 | 0.000039 |
| 489 | 0.12 | 0.12 | 0.030 | 1779.0 | -3.88 | 4.75 | 8.44 | 0.0 | 1.0000 | 18.128999 | ... | 12.882 | 11.710 | 10.977 | 22.719 | 19.781 | 17.869 | 16.276 | 15.153 | 3.300000 | 0.000132 |
| 490 | 0.12 | 0.12 | 0.040 | 2225.0 | -3.46 | 4.84 | 8.79 | 0.0 | 1.0000 | 16.322999 | ... | 11.268 | 10.580 | 10.210 | 21.486 | 18.909 | 16.300 | 14.352 | 13.157 | 3.300000 | 0.000347 |
| 491 | 0.12 | 0.12 | 0.050 | 2471.0 | -3.23 | 4.88 | 9.31 | 0.0 | 0.9970 | 15.099000 | ... | 10.655 | 10.068 | 9.746 | 19.253 | 17.727 | 14.750 | 13.173 | 12.261 | 3.298695 | 0.000589 |
| 492 | 0.12 | 0.12 | 0.060 | 2635.0 | -3.06 | 4.91 | 9.89 | 0.0 | 0.9010 | 14.358000 | ... | 10.289 | 9.710 | 9.397 | 17.811 | 16.792 | 13.812 | 12.518 | 11.764 | 3.254725 | 0.000871 |
| 493 | 0.12 | 0.12 | 0.070 | 2757.0 | -2.94 | 4.93 | 10.47 | 0.0 | 0.2440 | 13.784000 | ... | 10.011 | 9.431 | 9.125 | 16.576 | 15.826 | 13.134 | 12.043 | 11.410 | 2.687390 | 0.001148 |
| 494 | 0.12 | 0.12 | 0.072 | 2780.0 | -2.91 | 4.93 | 10.59 | 0.0 | 0.1110 | 13.674000 | ... | 9.959 | 9.379 | 9.073 | 16.387 | 15.630 | 13.008 | 11.952 | 11.340 | 2.345323 | 0.001230 |
| 495 | 0.12 | 0.12 | 0.075 | 2810.0 | -2.88 | 4.93 | 10.78 | 0.0 | 0.0171 | 13.533000 | ... | 9.884 | 9.304 | 9.001 | 16.173 | 15.393 | 12.851 | 11.833 | 11.247 | 1.532996 | 0.001318 |
| 496 | 0.12 | 0.12 | 0.080 | 2853.0 | -2.83 | 4.94 | 11.08 | 0.0 | 0.0001 | 13.363000 | ... | 9.771 | 9.192 | 8.896 | 15.990 | 15.143 | 12.668 | 11.679 | 11.119 | -0.700000 | 0.001479 |
| 497 | 0.12 | 0.12 | 0.090 | 2924.0 | -2.74 | 4.94 | 11.69 | 0.0 | 0.0000 | 13.058000 | ... | 9.569 | 8.992 | 8.707 | 15.614 | 14.704 | 12.346 | 11.408 | 10.891 | -inf | 0.001820 |
| 498 | 0.12 | 0.12 | 0.100 | 2978.0 | -2.67 | 4.95 | 12.25 | 0.0 | 0.0000 | 12.800000 | ... | 9.401 | 8.824 | 8.547 | 15.218 | 14.336 | 12.076 | 11.184 | 10.700 | -inf | 0.002138 |
| 499 | 0.12 | 0.12 | 0.200 | 3270.0 | -2.19 | 4.93 | 17.62 | 0.0 | 0.0000 | 11.203000 | ... | 8.332 | 7.736 | 7.483 | 12.818 | 12.050 | 10.505 | 9.871 | 9.538 | -inf | 0.006457 |
| 500 | 0.12 | 0.12 | 0.300 | 3411.0 | -1.91 | 4.90 | 22.28 | 0.0 | 0.0000 | 10.378000 | ... | 7.696 | 7.088 | 6.844 | 11.838 | 11.027 | 9.708 | 9.158 | 8.868 | -inf | 0.012303 |
| 501 | 0.12 | 0.12 | 0.400 | 3536.0 | -1.68 | 4.86 | 26.97 | 0.0 | 0.0000 | 9.696000 | ... | 7.167 | 6.546 | 6.310 | 11.042 | 10.207 | 9.056 | 8.573 | 8.315 | -inf | 0.020893 |
| 502 | 0.12 | 0.12 | 0.500 | 3727.0 | -1.42 | 4.78 | 33.02 | 0.0 | 0.0000 | 8.867000 | ... | 6.562 | 5.912 | 5.693 | 10.069 | 9.195 | 8.284 | 7.896 | 7.679 | -inf | 0.038019 |
| 503 | 0.12 | 0.12 | 0.600 | 4030.0 | -1.15 | 4.73 | 38.33 | 0.0 | 0.0000 | 8.019000 | ... | 6.001 | 5.316 | 5.139 | 9.077 | 8.145 | 7.524 | 7.255 | 7.085 | -inf | 0.070795 |
| 504 | 0.12 | 0.12 | 0.700 | 4400.0 | -0.88 | 4.68 | 43.93 | 0.0 | 0.0000 | 7.152000 | ... | 5.427 | 4.774 | 4.642 | 8.061 | 7.135 | 6.762 | 6.598 | 6.469 | -inf | 0.131826 |
| 505 | 0.12 | 0.12 | 0.800 | 4825.0 | -0.61 | 4.63 | 49.66 | 0.0 | 0.0046 | 6.326000 | ... | 4.883 | 4.352 | 4.246 | 7.006 | 6.284 | 6.040 | 5.940 | 5.854 | 0.962758 | 0.245471 |
| 506 | 0.12 | 0.12 | 0.900 | 5208.0 | -0.37 | 4.58 | 56.22 | 0.0 | 0.0822 | 5.641000 | ... | 4.399 | 3.967 | 3.877 | 6.174 | 5.604 | 5.426 | 5.366 | 5.315 | 2.214872 | 0.426580 |
| 507 | 0.12 | 0.12 | 1.000 | 5547.0 | -0.16 | 4.52 | 63.41 | 0.0 | 0.3070 | 5.065000 | ... | 3.974 | 3.614 | 3.536 | 5.503 | 5.038 | 4.904 | 4.873 | 4.848 | 2.787138 | 0.691831 |
| 508 | 0.12 | 0.12 | 1.100 | 5889.0 | 0.05 | 4.45 | 71.63 | 0.0 | 0.5740 | 4.514000 | ... | 3.560 | 3.264 | 3.195 | 4.875 | 4.498 | 4.403 | 4.398 | 4.396 | 3.058912 | 1.122018 |
| 509 | 0.12 | 0.12 | 1.200 | 6172.0 | 0.24 | 4.39 | 80.96 | 0.0 | 0.7700 | 4.038000 | ... | 3.187 | 2.936 | 2.872 | 4.341 | 4.032 | 3.967 | 3.983 | 3.996 | 3.186491 | 1.737801 |
| 510 | 0.12 | 0.12 | 1.300 | 6455.0 | 0.41 | 4.33 | 90.19 | 0.0 | 0.8800 | 3.605000 | ... | 2.853 | 2.643 | 2.583 | 3.855 | 3.609 | 3.574 | 3.610 | 3.634 | 3.244483 | 2.570396 |
| 511 | 0.12 | 0.12 | 1.400 | 6767.0 | 0.56 | 4.29 | 97.82 | 0.0 | 0.9380 | 3.226000 | ... | 2.576 | 2.408 | 2.352 | 3.422 | 3.242 | 3.240 | 3.297 | 3.331 | 3.272203 | 3.630781 |
24 rows × 22 columns
min(BTSettl_120['Teff'])
1304.0
max(BTSettl['Teff'])
6768.0
BTSettl_Li_isochrones = BTSettl_mod.BTSettl_Li_isochrones
BTSettl_Li_isochrones.keys()
dict_keys([0.001, 0.002, 0.003, 0.004, 0.005, 0.006, 0.007, 0.008, 0.009000000000000001, 0.01, 0.02, 0.03, 0.04, 0.05, 0.06, 0.07, 0.08, 0.09, 0.1, 0.12, 0.15, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7000000000000001, 0.8, 0.9, 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0])
len(BTSettl_Li_isochrones.keys())
39
BTSettl_Li_isochrones = {round(key, 3): value for key, value in BTSettl_Li_isochrones.items()}
BTSettl_Li_isochrones.keys()
dict_keys([0.001, 0.002, 0.003, 0.004, 0.005, 0.006, 0.007, 0.008, 0.009, 0.01, 0.02, 0.03, 0.04, 0.05, 0.06, 0.07, 0.08, 0.09, 0.1, 0.12, 0.15, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0])
len(BTSettl_Li_isochrones.keys())
39
BTSettl_Li_isochrones_Teff = BTSettl_Li_isochrones[0.120][BTSettl_Li_isochrones[0.120]['Teff'] < 2955]
BTSettl_Li_isochrones_Teff = BTSettl_Li_isochrones_Teff[BTSettl_Li_isochrones_Teff ['Teff'] > 1600]
BTSettl_Li_isochrones_Teff
| age_Gyr | t(Gyr) | M/Ms | Teff | log(L/Lsun) | lg(g) | R(Gcm) | D | Li | G_abs | ... | J_abs | H_abs | K_abs | g_abs | r_abs | i_abs | y_abs | z_abs | A(Li) | Lsun | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 489 | 0.12 | 0.12 | 0.030 | 1779.0 | -3.88 | 4.75 | 8.44 | 0.0 | 1.0000 | 18.128999 | ... | 12.882 | 11.710 | 10.977 | 22.719 | 19.781 | 17.869 | 16.276 | 15.153 | 3.300000 | 0.000132 |
| 490 | 0.12 | 0.12 | 0.040 | 2225.0 | -3.46 | 4.84 | 8.79 | 0.0 | 1.0000 | 16.322999 | ... | 11.268 | 10.580 | 10.210 | 21.486 | 18.909 | 16.300 | 14.352 | 13.157 | 3.300000 | 0.000347 |
| 491 | 0.12 | 0.12 | 0.050 | 2471.0 | -3.23 | 4.88 | 9.31 | 0.0 | 0.9970 | 15.099000 | ... | 10.655 | 10.068 | 9.746 | 19.253 | 17.727 | 14.750 | 13.173 | 12.261 | 3.298695 | 0.000589 |
| 492 | 0.12 | 0.12 | 0.060 | 2635.0 | -3.06 | 4.91 | 9.89 | 0.0 | 0.9010 | 14.358000 | ... | 10.289 | 9.710 | 9.397 | 17.811 | 16.792 | 13.812 | 12.518 | 11.764 | 3.254725 | 0.000871 |
| 493 | 0.12 | 0.12 | 0.070 | 2757.0 | -2.94 | 4.93 | 10.47 | 0.0 | 0.2440 | 13.784000 | ... | 10.011 | 9.431 | 9.125 | 16.576 | 15.826 | 13.134 | 12.043 | 11.410 | 2.687390 | 0.001148 |
| 494 | 0.12 | 0.12 | 0.072 | 2780.0 | -2.91 | 4.93 | 10.59 | 0.0 | 0.1110 | 13.674000 | ... | 9.959 | 9.379 | 9.073 | 16.387 | 15.630 | 13.008 | 11.952 | 11.340 | 2.345323 | 0.001230 |
| 495 | 0.12 | 0.12 | 0.075 | 2810.0 | -2.88 | 4.93 | 10.78 | 0.0 | 0.0171 | 13.533000 | ... | 9.884 | 9.304 | 9.001 | 16.173 | 15.393 | 12.851 | 11.833 | 11.247 | 1.532996 | 0.001318 |
| 496 | 0.12 | 0.12 | 0.080 | 2853.0 | -2.83 | 4.94 | 11.08 | 0.0 | 0.0001 | 13.363000 | ... | 9.771 | 9.192 | 8.896 | 15.990 | 15.143 | 12.668 | 11.679 | 11.119 | -0.700000 | 0.001479 |
| 497 | 0.12 | 0.12 | 0.090 | 2924.0 | -2.74 | 4.94 | 11.69 | 0.0 | 0.0000 | 13.058000 | ... | 9.569 | 8.992 | 8.707 | 15.614 | 14.704 | 12.346 | 11.408 | 10.891 | -inf | 0.001820 |
9 rows × 22 columns
BTSettl_Li_isochrones_Teff['BP_abs']-BTSettl_Li_isochrones_Teff['RP_abs']
489 4.853999 490 5.589999 491 5.210000 492 4.732000 493 4.146000 494 4.050000 495 3.950000 496 3.889000 497 3.759000 dtype: float64
MIST¶
mist_instance = models.MIST(path_all)
phot = 'UBVRIplus'
feh = 'm0.25'
vvcrit = 0.0
mist_instance.file_copy(phot, vvcrit, feh)
phot_G2 = 'UBVRIplus'
phot_P = 'PanSTARRS'
feh = 'p0.00'
vvcrit = 0.0
MIST_FULL = mist_instance.read_iso(phot_G2, phot_P, vvcrit, feh)
phot_G2 = 'UBVRIplus'
phot_P = 'PanSTARRS'
feh = 'p0.25'
vvcrit = 0.0
MIST_FULL_2 = mist_instance.read_iso(phot_G2, phot_P, vvcrit, feh)
desired_age = 0.120
nearest_age = select_nearest_age(MIST_FULL, desired_age)
MIST_FULL[nearest_age]
Reading in: /pcdisk/dalen/lgonzalez/OneDrive/Escritorio/CAB_INTA-CSIC/01-Doctorado/013-Jupyter/0134-biosc_env/data/MIST_v1.2_feh_p0.00_afe_p0.0_vvcrit0.0_PanSTARRS.iso.cmd
Reading in: /pcdisk/dalen/lgonzalez/OneDrive/Escritorio/CAB_INTA-CSIC/01-Doctorado/013-Jupyter/0134-biosc_env/data/MIST_v1.2_feh_p0.00_afe_p0.0_vvcrit0.0_UBVRIplus.iso.cmd
Reading in: /pcdisk/dalen/lgonzalez/OneDrive/Escritorio/CAB_INTA-CSIC/01-Doctorado/013-Jupyter/0134-biosc_env/data/MIST_v1.2_vvcrit0.0_full_isos/MIST_v1.2_feh_p0.00_afe_p0.0_vvcrit0.0_full.iso
version: {'MIST': '1.2', 'MESA': '7503'}
abundances: {'Yinit': 0.2703, 'Zinit': 0.0142857, '[Fe/H]': 0.0, '[a/Fe]': 0.0}
rotation: 0.0
Reading in: /pcdisk/dalen/lgonzalez/OneDrive/Escritorio/CAB_INTA-CSIC/01-Doctorado/013-Jupyter/0134-biosc_env/data/MIST_v1.2_feh_p0.25_afe_p0.0_vvcrit0.0_PanSTARRS.iso.cmd
Reading in: /pcdisk/dalen/lgonzalez/OneDrive/Escritorio/CAB_INTA-CSIC/01-Doctorado/013-Jupyter/0134-biosc_env/data/MIST_v1.2_feh_p0.25_afe_p0.0_vvcrit0.0_UBVRIplus.iso.cmd
Reading in: /pcdisk/dalen/lgonzalez/OneDrive/Escritorio/CAB_INTA-CSIC/01-Doctorado/013-Jupyter/0134-biosc_env/data/MIST_v1.2_vvcrit0.0_full_isos/MIST_v1.2_feh_p0.25_afe_p0.0_vvcrit0.0_full.iso
version: {'MIST': '1.2', 'MESA': '7503'}
abundances: {'Yinit': 0.2869, 'Zinit': 0.0254039, '[Fe/H]': 0.25, '[a/Fe]': 0.0}
rotation: 0.0
| EEP | log10_isochrone_age_yr | initial_mass | M/Ms | log_Teff | log_g | log_L | [Fe/H]_init | [Fe/H] | g | ... | Gaia_BP_MAWf | Gaia_RP_MAW | TESS | G | BP | RP | phase | surface_li7 | Teff | Lsun | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 153 | 8.1 | 0.100000 | 0.100000 | 3.472358 | 4.943898 | -2.661675 | 0.0 | 0.041367 | 15.182353 | ... | 14.818656 | 11.510085 | 11.390598 | 12.816993 | 14.800249 | 11.507612 | -1.0 | 4.509661e-20 | 2967.276575 | 0.002179 |
| 1 | 154 | 8.1 | 0.100487 | 0.100487 | 3.472502 | 4.943863 | -2.659350 | 0.0 | 0.041366 | 15.172418 | ... | 14.808554 | 11.503003 | 11.383737 | 12.809403 | 14.790139 | 11.500522 | -1.0 | 4.626529e-20 | 2968.258909 | 0.002191 |
| 2 | 155 | 8.1 | 0.104821 | 0.104821 | 3.473925 | 4.943235 | -2.637765 | 0.0 | 0.041366 | 15.077920 | ... | 14.712386 | 11.436582 | 11.319491 | 12.737943 | 14.693890 | 11.434015 | -1.0 | 5.808929e-20 | 2977.999206 | 0.002303 |
| 3 | 156 | 8.1 | 0.109198 | 0.109198 | 3.475536 | 4.942625 | -2.615297 | 0.0 | 0.041368 | 14.976478 | ... | 14.609145 | 11.366608 | 11.251957 | 12.662225 | 14.590545 | 11.363955 | -1.0 | 7.267820e-20 | 2989.068594 | 0.002425 |
| 4 | 157 | 8.1 | 0.113606 | 0.113606 | 3.477285 | 4.942037 | -2.592188 | 0.0 | 0.041368 | 14.870311 | ... | 14.501178 | 11.294242 | 11.182213 | 12.583573 | 14.482453 | 11.291506 | -1.0 | 9.055154e-20 | 3001.132776 | 0.002557 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 357 | 1706 | 8.1 | 4.873859 | 0.879580 | 4.653539 | 8.391392 | -0.440251 | 0.0 | 0.097281 | 9.765747 | ... | 9.720577 | 10.274443 | 10.278122 | 9.957335 | 9.783835 | 10.293779 | 6.0 | 4.627805e-19 | 45033.880846 | 0.362869 |
| 358 | 1707 | 8.1 | 4.875869 | 0.879720 | 4.645607 | 8.393120 | -0.473640 | 0.0 | 0.097241 | 9.789981 | ... | 9.745432 | 10.297119 | 10.300766 | 9.981316 | 9.808478 | 10.316435 | 6.0 | 4.579056e-19 | 44218.761646 | 0.336016 |
| 359 | 1708 | 8.1 | 4.878084 | 0.879874 | 4.637685 | 8.394857 | -0.506986 | 0.0 | 0.097196 | 9.814415 | ... | 9.770496 | 10.319976 | 10.323583 | 10.005491 | 9.833328 | 10.339262 | 6.0 | 4.529060e-19 | 43419.503570 | 0.311182 |
| 360 | 1709 | 8.1 | 4.880514 | 0.880044 | 4.629777 | 8.396619 | -0.540296 | 0.0 | 0.097147 | 9.839218 | ... | 9.795950 | 10.343163 | 10.346726 | 10.030030 | 9.858564 | 10.362416 | 6.0 | 4.457822e-19 | 42636.017717 | 0.288207 |
| 361 | 1710 | 8.1 | 4.883200 | 0.880231 | 4.621878 | 8.398361 | -0.573538 | 0.0 | 0.097092 | 9.864440 | ... | 9.821851 | 10.366711 | 10.370228 | 10.054979 | 9.884237 | 10.385928 | 6.0 | 4.363275e-19 | 41867.616468 | 0.266970 |
362 rows × 45 columns
[Fe/H] for Pleiades: +0.042 (Soderblom et al. 2009).
MIST_FULL[nearest_age].columns
Index(['EEP', 'log10_isochrone_age_yr', 'initial_mass', 'M/Ms', 'log_Teff',
'log_g', 'log_L', '[Fe/H]_init', '[Fe/H]', 'g', 'r', 'i', 'z', 'y', 'w',
'PS_open', 'phase', 'Bessell_U', 'Bessell_B', 'Bessell_V', 'Bessell_R',
'Bessell_I', 'J', 'H', 'K', 'Kepler_Kp', 'Kepler_D51', 'Hipparcos_Hp',
'Tycho_B', 'Tycho_V', 'Gaia_G_DR2Rev', 'Gaia_BP_DR2Rev',
'Gaia_RP_DR2Rev', 'Gaia_G_MAW', 'Gaia_BP_MAWb', 'Gaia_BP_MAWf',
'Gaia_RP_MAW', 'TESS', 'G', 'BP', 'RP', 'phase', 'surface_li7', 'Teff',
'Lsun'],
dtype='object')
plt.rcParams.update({'font.size': 14}) # Set the font size
fig, ax = plt.subplots(1, 1, figsize=(8, 6))
ax.set_ylabel('Mass Fraction Li')
ax.set_xlabel('$T_{eff}$ [K]')
ax.plot(10**MIST_FULL[nearest_age]['log_Teff'], MIST_FULL[nearest_age]['surface_li7'])
ax.set_xlim(2500, 10000)
ax.invert_xaxis()
max(10**MIST_FULL[nearest_age]['log_Teff'])
339646.6795258933
min(10**MIST_FULL[nearest_age]['log_Teff'])
2967.2765754069314
minTeff_array = []
for key in MIST_FULL.keys():
minTeff = min(10**MIST_FULL[key]['log_Teff'])
minTeff_array.append(minTeff)
min(minTeff_array)
2885.5722587919586
SPOTS¶
plt.rcParams.update({'font.size': 11, 'axes.linewidth': 1, 'axes.edgecolor': 'k'})
plt.rcParams['font.family'] = 'serif'
spots_instance = models.SPOTS(path_all)
SPOTS = spots_instance.SPOTS
SPOTS.keys()
dict_keys(['f017', 'f085', 'f051', 'f034', 'f068', 'f000'])
SPOTS['f000'].keys()
dict_keys([0.001, 0.002, 0.003, 0.004, 0.005, 0.006, 0.007, 0.008, 0.009, 0.01, 0.011, 0.013, 0.014, 0.016, 0.018, 0.02, 0.022, 0.025, 0.028, 0.032, 0.035, 0.04, 0.045, 0.05, 0.056, 0.063, 0.071, 0.079, 0.089, 0.1, 0.112, 0.126, 0.141, 0.158, 0.178, 0.2, 0.224, 0.251, 0.282, 0.316, 0.355, 0.398, 0.447, 0.501, 0.562, 0.631, 0.708, 0.794, 0.891, 1.0, 1.122, 1.259, 1.413, 1.585, 1.778, 1.995, 2.239, 2.512, 2.818, 3.162, 3.548, 3.981])
SPOTS['f000'][0.126].columns
Index(['Mass', 'Fspot', 'Xspot', 'log(L/Lsun)', 'log(R/Rsun)', 'log(g)',
'log(Teff)', 'log(T_hot)', 'log(T_cool)', 'TauCZ', 'Li/Li0', 'B_mag',
'V_mag', 'Rc_mag', 'Ic_mag', 'J_mag', 'H_mag', 'K_mag', 'W1_mag',
'G_mag', 'BP_mag', 'RP_mag', 'Age [Gyr]', 'A(Li)', 'M/Ms', 'Teff',
'Lsun', 'G', 'BP', 'RP'],
dtype='object')
SPOTS['f085'][0.035]['BP_mag']-SPOTS['f085'][0.035]['RP_mag']
0 NaN 1 NaN 2 NaN 3 NaN 4 NaN 5 NaN 6 NaN 7 NaN 8 NaN 9 NaN 10 2.5453 11 2.4617 12 2.3605 13 2.1510 14 1.8895 15 1.6275 16 1.3964 17 1.2118 18 1.0748 19 0.9690 20 0.9308 21 0.8991 22 0.8394 23 0.7704 24 0.6691 dtype: float64
SPOTS['f085'][0.035]['Li/Li0']
0 0.99994 1 0.99501 2 0.91175 3 0.44079 4 0.01100 5 0.00002 6 0.00020 7 0.00404 8 0.03503 9 0.13395 10 0.29661 11 0.46932 12 0.61882 13 0.72710 14 0.79927 15 0.84324 16 0.87046 17 0.89569 18 0.91984 19 0.94082 20 0.95712 21 0.96947 22 0.97850 23 0.98504 24 0.98964 Name: Li/Li0, dtype: float64
fig, ax = plt.subplots(1, 1, figsize=(8, 6))
ax.set_ylabel('$Li/Li^0$')
ax.set_xlabel('$B_p-R_p$ [mag]')
ax.plot(SPOTS['f000'][0.025]['BP_mag']-SPOTS['f000'][0.025]['RP_mag'], SPOTS['f000'][0.025]['Li/Li0'], linewidth=1, label='SPOTS f000; 25 Myr')
ax.plot(SPOTS['f017'][0.006]['BP_mag']-SPOTS['f017'][0.006]['RP_mag'], SPOTS['f017'][0.006]['Li/Li0'], linewidth=1, label='SPOTS f017; 6 Myr')
ax.plot(SPOTS['f034'][0.010]['BP_mag']-SPOTS['f034'][0.010]['RP_mag'], SPOTS['f034'][0.010]['Li/Li0'], linewidth=1, label='SPOTS f034; 10 Myr')
ax.plot(SPOTS['f051'][0.016]['BP_mag']-SPOTS['f051'][0.016]['RP_mag'], SPOTS['f051'][0.016]['Li/Li0'], linewidth=1, label='SPOTS f051; 16 Myr')
ax.plot(SPOTS['f068'][0.028]['BP_mag']-SPOTS['f068'][0.028]['RP_mag'], SPOTS['f068'][0.028]['Li/Li0'], linewidth=1, label='SPOTS f068; 28 Myr')
ax.plot(SPOTS['f085'][0.035]['BP_mag']-SPOTS['f085'][0.035]['RP_mag'], SPOTS['f085'][0.035]['Li/Li0'], linewidth=1, label='SPOTS f085; 35 Myr')
ax.plot(SPOTS['f000'][0.126]['BP_mag']-SPOTS['f000'][0.126]['RP_mag'], SPOTS['f000'][0.126]['Li/Li0'], linewidth=1, linestyle='--', label='SPOTS f000; 126 Myr')
#ax.errorbar(data_obs_Pleiades['Teff'], data_obs_Pleiades['ALi'], yerr=data_obs_Pleiades['e_ALi'], fmt='.', zorder=0, color='r', elinewidth=1, capsize=2)
ax.legend(fontsize=12, loc='upper center', bbox_to_anchor=(1.25, 0.9))
<matplotlib.legend.Legend at 0x7fd70745e450>
fig, ax = plt.subplots(1, 1, figsize=(8, 6))
ax.set_ylabel('$Li/Li^0$')
ax.set_xlabel('$G-R_p$ [mag]')
ax.plot(SPOTS['f000'][0.025]['G_mag']-SPOTS['f000'][0.025]['RP_mag'], np.log10(SPOTS['f000'][0.025]['Li/Li0'])+3.3, linewidth=1, label='SPOTS f000; 25 Myr')
ax.plot(SPOTS['f017'][0.006]['G_mag']-SPOTS['f017'][0.006]['RP_mag'], np.log10(SPOTS['f017'][0.006]['Li/Li0'])+3.3, linewidth=1, label='SPOTS f017; 6 Myr')
ax.plot(SPOTS['f034'][0.010]['G_mag']-SPOTS['f034'][0.010]['RP_mag'], np.log10(SPOTS['f034'][0.010]['Li/Li0'])+3.3, linewidth=1, label='SPOTS f034; 10 Myr')
ax.plot(SPOTS['f051'][0.016]['G_mag']-SPOTS['f051'][0.016]['RP_mag'], np.log10(SPOTS['f051'][0.016]['Li/Li0'])+3.3, linewidth=1, label='SPOTS f051; 16 Myr')
ax.plot(SPOTS['f068'][0.028]['G_mag']-SPOTS['f068'][0.028]['RP_mag'], np.log10(SPOTS['f068'][0.028]['Li/Li0'])+3.3, linewidth=1, label='SPOTS f068; 28 Myr')
ax.plot(SPOTS['f085'][0.035]['G_mag']-SPOTS['f085'][0.035]['RP_mag'], np.log10(SPOTS['f085'][0.035]['Li/Li0'])+3.3, linewidth=1, label='SPOTS f085; 35 Myr')
ax.plot(SPOTS['f000'][0.126]['G_mag']-SPOTS['f000'][0.126]['RP_mag'], np.log10(SPOTS['f000'][0.126]['Li/Li0'])+3.3, linewidth=1, linestyle='--', label='SPOTS f000; 126 Myr')
ax.errorbar(data_obs_Pleiades['G_abs']-data_obs_Pleiades['RP_abs'], data_obs_Pleiades['ALi'], yerr=data_obs_Pleiades['e_ALi'], fmt='.', zorder=0, color='r', elinewidth=1, capsize=2)
ax.legend(fontsize=12, loc='upper center', bbox_to_anchor=(1.25, 0.9))
/pcdisk/dalen/lgonzalez/OneDrive/Escritorio/CAB_INTA-CSIC/01-Doctorado/013-Jupyter/0134-biosc_env/biosc_env/lib64/python3.11/site-packages/pandas/core/arraylike.py:399: RuntimeWarning: divide by zero encountered in log10 result = getattr(ufunc, method)(*inputs, **kwargs)
<matplotlib.legend.Legend at 0x7fd7276c9210>
fig, ax = plt.subplots(1, 1, figsize=(8, 6))
ax.set_ylabel('$G$ [mag]')
ax.set_xlabel('$B_p-R_p$ [mag]')
ax.plot(SPOTS['f000'][0.126]['BP_mag']-SPOTS['f000'][0.126]['RP_mag'], SPOTS['f000'][0.126]['G_mag'], linewidth=1, label='SPOTS f000; 126 Myr', color='k')
ax.plot(SPOTS['f051'][0.126]['BP_mag']-SPOTS['f051'][0.126]['RP_mag'], SPOTS['f051'][0.126]['G_mag'], linewidth=1, linestyle='--', label='SPOTS f051; 126 Myr', color='k')
ax.plot(SPOTS['f085'][0.126]['BP_mag']-SPOTS['f085'][0.126]['RP_mag'], SPOTS['f085'][0.126]['G_mag'], linewidth=1, linestyle=':', label='SPOTS f085; 126 Myr', color='k')
ax.errorbar(data_obs_Pleiades['BP_abs']-data_obs_Pleiades['RP_abs'], data_obs_Pleiades['G_abs'], yerr=data_obs_Pleiades['e_g'], fmt='.', zorder=0, color='r', elinewidth=1, capsize=2, alpha=0.125)
ax.legend(fontsize=12, loc='upper center', bbox_to_anchor=(1.25, 0.9))
ax.invert_yaxis()
fig, ax = plt.subplots(1, 1, figsize=(8, 6))
ax.set_ylabel('$G$ [mag]')
ax.set_xlabel('$G-J$ [mag]')
ax.plot(SPOTS['f000'][0.126]['G_mag']-SPOTS['f000'][0.126]['J_mag'], SPOTS['f000'][0.126]['G_mag'], linewidth=1, label='SPOTS f000; 126 Myr', color='k')
ax.plot(SPOTS['f051'][0.126]['G_mag']-SPOTS['f051'][0.126]['J_mag'], SPOTS['f051'][0.126]['G_mag'], linewidth=1, linestyle='--', label='SPOTS f051; 126 Myr', color='k')
ax.plot(SPOTS['f085'][0.126]['G_mag']-SPOTS['f085'][0.126]['J_mag'], SPOTS['f085'][0.126]['G_mag'], linewidth=1, linestyle=':', label='SPOTS f085; 126 Myr', color='k')
ax.errorbar(data_obs_Pleiades['G_abs']-data_obs_Pleiades['J_abs'], data_obs_Pleiades['G_abs'], yerr=data_obs_Pleiades['e_g'], fmt='.', zorder=0, color='r', elinewidth=1, capsize=2, alpha=0.125)
ax.legend(fontsize=12, loc='upper center', bbox_to_anchor=(1.25, 0.9))
ax.invert_yaxis()
Tsun = 5772
fig, ax = plt.subplots(1, 1, figsize=(8, 6))
ax.set_ylabel('$A(Li)$ [dex]')
ax.set_xlabel('$T_{eff}$ [K]')
ax.plot(10**SPOTS['f000'][0.126]['log(Teff)'], SPOTS['f000'][0.126]['A(Li)'], linewidth=1, label='SPOTS f000')
ax.plot(10**SPOTS['f017'][0.126]['log(Teff)'], SPOTS['f017'][0.126]['A(Li)'], linewidth=1, label='SPOTS f017')
ax.plot(10**SPOTS['f034'][0.126]['log(Teff)'], SPOTS['f034'][0.126]['A(Li)'], linewidth=1, label='SPOTS f034')
ax.plot(10**SPOTS['f051'][0.126]['log(Teff)'], SPOTS['f051'][0.126]['A(Li)'], linewidth=1, label='SPOTS f051')
ax.errorbar(Tsun, solar_abundance, xerr=0.5, yerr=e_solar_abundance, fmt='.', zorder=2, color='b', elinewidth=1, capsize=0)
ax.plot(BTSettl_Li_isochrones[5]['Teff'], BTSettl_Li_isochrones[5]['A(Li)'], linewidth=1, linestyle='--', color='b')
ax.plot(BTSettl_Li_isochrones[0.120]['Teff'], BTSettl_Li_isochrones[0.120]['A(Li)'], linewidth=1, label='BT-Settl', linestyle='--', color='k')
ax.errorbar(data_obs_Pleiades['Teff'], data_obs_Pleiades['ALi'], yerr=data_obs_Pleiades['e_ALi'], fmt='.', zorder=0, color='r', elinewidth=1, capsize=2)
ax.legend(fontsize=12)
ax.invert_xaxis()
fig, ax = plt.subplots(1, 1, figsize=(8, 6))
ax.set_ylabel('$A(Li)$ [dex]')
ax.set_xlabel('G-J [mag]')
ax.plot(SPOTS['f000'][0.126]['G_mag']-SPOTS['f000'][0.126]['J_mag'], SPOTS['f000'][0.126]['A(Li)'], linewidth=1, label='SPOTS f000')
ax.plot(SPOTS['f017'][0.126]['G_mag']-SPOTS['f000'][0.126]['J_mag'], SPOTS['f017'][0.126]['A(Li)'], linewidth=1, label='SPOTS f017')
ax.plot(SPOTS['f034'][0.126]['G_mag']-SPOTS['f000'][0.126]['J_mag'], SPOTS['f034'][0.126]['A(Li)'], linewidth=1, label='SPOTS f034')
ax.plot(SPOTS['f051'][0.126]['G_mag']-SPOTS['f000'][0.126]['J_mag'], SPOTS['f051'][0.126]['A(Li)'], linewidth=1, label='SPOTS f051')
ax.plot(BTSettl_Li_isochrones[0.120]['G_abs']-BTSettl_Li_isochrones[0.120]['J_abs'], BTSettl_Li_isochrones[0.120]['A(Li)'], linewidth=1, label='BT-Settl', linestyle='--', color='k')
ax.errorbar(data_obs_Pleiades['g']-data_obs_Pleiades['Jmag'], data_obs_Pleiades['ALi'], yerr=data_obs_Pleiades['e_ALi'], fmt='.', zorder=0, color='r', elinewidth=1, capsize=2)
ax.legend(fontsize=12)
<matplotlib.legend.Legend at 0x7fd71c291210>
data_obs_Pleiades[(~data_obs_Pleiades['ALi'].isnull()) & (data_obs_Pleiades['ALi'] != 0)]['ALi'].count()
99
fig, ax = plt.subplots(1, 1, figsize=(8, 6))
ax.set_ylabel('$A(Li)$ [dex]')
ax.set_xlabel('G-J [mag]')
ax.plot(SPOTS['f017'][0.126]['G_mag']-SPOTS['f017'][0.126]['J_mag'], SPOTS['f017'][0.126]['A(Li)'], linewidth=1, label='SPOTS')
ax.plot(BTSettl_Li_isochrones[0.120]['G_abs']-BTSettl_Li_isochrones[0.120]['J_abs'], BTSettl_Li_isochrones[0.120]['A(Li)'], linewidth=1, label='BT-Settl')
ax.errorbar(data_obs_Pleiades['g']-data_obs_Pleiades['Jmag'], data_obs_Pleiades['ALi'], yerr=data_obs_Pleiades['e_ALi'], fmt='.', zorder=0, color='r', elinewidth=1, capsize=2)
ax.legend()
<matplotlib.legend.Legend at 0x7fd713d49590>
max(10**SPOTS['f000'][0.126]['log(Teff)'])
6512.426277507005
min(10**SPOTS['f000'][0.126]['log(Teff)'])
2949.742462182295
BHAC15¶
file_path = path_all + 'data/BHAC15_iso.GAIA.txt'
BHAC15_dict = models.BHAC15.parse_file(file_path)
csv_file_path = path_all + 'data/BHAC15_iso.GAIA.csv'
models.BHAC15.save_to_csv(BHAC15_dict, file_path)
Corrected photometry SPOTS models¶
plt.rcParams.update({'font.size': 11, 'axes.linewidth': 1, 'axes.edgecolor': 'k'})
plt.rcParams['font.family'] = 'serif'
spots_instance = models.SPOTS_YBC(path_all)
SPOTS_edr3 = spots_instance.SPOTS_edr3
spots_f000_edr3 = spots_instance.spots_f000_edr3
desired_age = 0.120
nearest_age = select_nearest_age(SPOTS_edr3['00'], desired_age)
SPOTS_edr3['00'][nearest_age]
| logAge | Mass | Fspot | Xspot | log(L/Lsun) | log(R/Rsun) | log(g) | log(Teff) | log(T_hot) | log(T_cool) | ... | G-J | G-RP | J-H | H-K | G-H | G-K | G-V | A(Li) | Lsun | Teff | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 8.1 | 0.10 | 0.0 | 0.8 | -2.689492 | -0.761060 | 4.959872 | 3.469784 | 3.469784 | 0.0 | ... | 3.35304 | 1.30207 | 0.59756 | 0.26728 | 0.0 | 4.21788 | -2.47892 | -inf | 0.002044 | 2949.742462 |
| 1 | 8.1 | 0.15 | 0.0 | 0.8 | -2.395952 | -0.669096 | 4.952036 | 3.497187 | 3.497187 | 0.0 | ... | 2.99943 | 1.20822 | 0.60105 | 0.25040 | 0.0 | 3.85088 | -1.73221 | -inf | 0.004018 | 3141.861887 |
| 2 | 8.1 | 0.20 | 0.0 | 0.8 | -2.201137 | -0.599432 | 4.937648 | 3.511059 | 3.511059 | 0.0 | ... | 2.84699 | 1.16439 | 0.60451 | 0.24098 | 0.0 | 3.69248 | -1.43388 | -inf | 0.006293 | 3243.838470 |
| 3 | 8.1 | 0.25 | 0.0 | 0.8 | -2.050513 | -0.543744 | 4.923181 | 3.520871 | 3.520871 | 0.0 | ... | 2.74490 | 1.13382 | 0.60821 | 0.23494 | 0.0 | 3.58805 | -1.28022 | -inf | 0.008902 | 3317.959343 |
| 4 | 8.1 | 0.30 | 0.0 | 0.8 | -1.925703 | -0.496960 | 4.908773 | 3.528682 | 3.528682 | 0.0 | ... | 2.66759 | 1.11005 | 0.61198 | 0.23084 | 0.0 | 3.51041 | -1.18325 | -inf | 0.011866 | 3378.171160 |
| 5 | 8.1 | 0.35 | 0.0 | 0.8 | -1.810935 | -0.454479 | 4.890757 | 3.536133 | 3.536133 | 0.0 | ... | 2.59808 | 1.08829 | 0.61570 | 0.22728 | 0.0 | 3.44106 | -1.10319 | -inf | 0.015455 | 3436.631053 |
| 6 | 8.1 | 0.40 | 0.0 | 0.8 | -1.690418 | -0.414652 | 4.869095 | 3.546349 | 3.546349 | 0.0 | ... | 2.50653 | 1.05926 | 0.62064 | 0.22274 | 0.0 | 3.34991 | -1.00765 | -inf | 0.020398 | 3518.427448 |
| 7 | 8.1 | 0.45 | 0.0 | 0.8 | -1.560148 | -0.373196 | 4.837337 | 3.558189 | 3.558189 | 0.0 | ... | 2.40336 | 1.02586 | 0.62677 | 0.21722 | 0.0 | 3.24735 | -0.92722 | -inf | 0.027533 | 3615.667953 |
| 8 | 8.1 | 0.50 | 0.0 | 0.8 | -1.414063 | -0.329963 | 4.796627 | 3.573093 | 3.573093 | 0.0 | ... | 2.27286 | 0.98262 | 0.63555 | 0.20717 | 0.0 | 3.11558 | -0.81893 | -inf | 0.038542 | 3741.906660 |
| 9 | 8.1 | 0.55 | 0.0 | 0.8 | -1.268473 | -0.291167 | 4.760429 | 3.590093 | 3.590093 | 0.0 | ... | 2.13329 | 0.93498 | 0.64821 | 0.18849 | 0.0 | 2.96999 | -0.68095 | -inf | 0.053892 | 3891.281506 |
| 10 | 8.1 | 0.60 | 0.0 | 0.8 | -1.152988 | -0.260413 | 4.736709 | 3.603587 | 3.603587 | 0.0 | ... | 2.03715 | 0.90024 | 0.65799 | 0.16852 | 0.0 | 2.86366 | -0.61492 | -inf | 0.070309 | 4014.089117 |
| 11 | 8.1 | 0.65 | 0.0 | 0.8 | -1.012628 | -0.226149 | 4.702943 | 3.621545 | 3.621545 | 0.0 | ... | 1.91598 | 0.85397 | 0.66084 | 0.14166 | 0.0 | 2.71848 | -0.55648 | -inf | 0.097134 | 4183.549674 |
| 12 | 8.1 | 0.70 | 0.0 | 0.8 | -0.864977 | -0.195221 | 4.673272 | 3.642994 | 3.642994 | 0.0 | ... | 1.76134 | 0.79178 | 0.64193 | 0.11210 | 0.0 | 2.51537 | -0.46759 | -0.421246 | 0.136466 | 4395.352697 |
| 13 | 8.1 | 0.75 | 0.0 | 0.8 | -0.728511 | -0.169368 | 4.651529 | 3.664184 | 3.664184 | 0.0 | ... | 1.60323 | 0.72951 | 0.58685 | 0.09376 | 0.0 | 2.28384 | -0.40592 | 1.075974 | 0.186848 | 4615.127249 |
| 14 | 8.1 | 0.80 | 0.0 | 0.8 | -0.597804 | -0.145327 | 4.631475 | 3.684840 | 3.684840 | 0.0 | ... | 1.46156 | 0.67394 | 0.51835 | 0.08276 | 0.0 | 2.06267 | -0.34286 | 1.881039 | 0.252462 | 4839.938464 |
| 15 | 8.1 | 0.85 | 0.0 | 0.8 | -0.474711 | -0.121780 | 4.610710 | 3.703839 | 3.703839 | 0.0 | ... | 1.34257 | 0.62656 | 0.46092 | 0.07325 | 0.0 | 1.87674 | -0.30101 | 2.349644 | 0.335189 | 5056.377615 |
| 16 | 8.1 | 0.90 | 0.0 | 0.8 | -0.358181 | -0.098047 | 4.588069 | 3.721106 | 3.721106 | 0.0 | ... | 1.24022 | 0.58621 | 0.41165 | 0.06548 | 0.0 | 1.71735 | -0.26414 | 2.641553 | 0.438348 | 5261.455359 |
| 17 | 8.1 | 0.95 | 0.0 | 0.8 | -0.246858 | -0.073722 | 4.562899 | 3.736774 | 3.736774 | 0.0 | ... | 1.15234 | 0.55188 | 0.36945 | 0.05927 | 0.0 | 1.58106 | -0.24622 | 2.831645 | 0.566424 | 5454.735418 |
| 18 | 8.1 | 1.00 | 0.0 | 0.8 | -0.140140 | -0.048660 | 4.535051 | 3.750922 | 3.750922 | 0.0 | ... | 1.07435 | 0.52027 | 0.33460 | 0.05284 | 0.0 | 1.46179 | -0.22926 | 2.958403 | 0.724203 | 5635.369148 |
| 19 | 8.1 | 1.05 | 0.0 | 0.8 | -0.037916 | -0.022712 | 4.504345 | 3.763504 | 3.763504 | 0.0 | ... | 1.00962 | 0.49384 | 0.30534 | 0.04858 | 0.0 | 1.36354 | -0.20177 | 3.047699 | 0.916397 | 5801.018360 |
| 20 | 8.1 | 1.10 | 0.0 | 0.8 | 0.060162 | 0.003835 | 4.471454 | 3.774750 | 3.774750 | 0.0 | ... | 0.94841 | 0.46791 | 0.27898 | 0.04458 | 0.0 | 1.27197 | -0.17662 | 3.113608 | 1.148582 | 5953.197490 |
| 21 | 8.1 | 1.15 | 0.0 | 0.8 | 0.154305 | 0.029990 | 4.438448 | 3.785208 | 3.785208 | 0.0 | ... | 0.89284 | 0.44356 | 0.25601 | 0.04084 | 0.0 | 1.18969 | -0.17206 | 3.162471 | 1.426608 | 6098.292624 |
| 22 | 8.1 | 1.20 | 0.0 | 0.8 | 0.244443 | 0.055355 | 4.406203 | 3.795061 | 3.795061 | 0.0 | ... | 0.84260 | 0.42131 | 0.23493 | 0.03804 | 0.0 | 1.11557 | -0.16850 | 3.199054 | 1.755672 | 6238.221627 |
| 23 | 8.1 | 1.25 | 0.0 | 0.8 | 0.330458 | 0.079603 | 4.375435 | 3.804440 | 3.804440 | 0.0 | ... | 0.79458 | 0.39976 | 0.21466 | 0.03575 | 0.0 | 1.04499 | -0.16294 | 3.226090 | 2.140217 | 6374.414055 |
| 24 | 8.1 | 1.30 | 0.0 | 0.8 | 0.412322 | 0.101930 | 4.347814 | 3.813743 | 3.813743 | 0.0 | ... | 0.74710 | 0.37807 | 0.19477 | 0.03369 | 0.0 | 0.97556 | -0.15675 | 3.246089 | 2.584178 | 6512.426278 |
25 rows × 60 columns
SPOTS_edr3['85'][nearest_age]
| logAge | Mass | Fspot | Xspot | log(L/Lsun) | log(R/Rsun) | log(g) | log(Teff) | log(T_hot) | log(T_cool) | ... | G-J | G-RP | J-H | H-K | G-H | G-K | G-V | A(Li) | Lsun | Teff | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 8.1 | 0.10 | 0.847 | 0.8 | -2.761755 | -0.665781 | 4.769315 | 3.404079 | 3.479336 | 3.382426 | ... | 4.107711 | 1.451015 | 0.585361 | 0.295944 | 0.0 | 4.989016 | -2.516235 | 2.244433 | 0.001731 | 2535.588947 |
| 1 | 8.1 | 0.15 | 0.847 | 0.8 | -2.497531 | -0.574975 | 4.763794 | 3.424732 | 3.499990 | 3.403080 | ... | 4.075011 | 1.437369 | 0.585551 | 0.295543 | 0.0 | 4.956105 | -1.866302 | -inf | 0.003180 | 2659.084019 |
| 2 | 8.1 | 0.20 | 0.847 | 0.8 | -2.314691 | -0.509727 | 4.758237 | 3.437818 | 3.513075 | 3.416165 | ... | 4.010649 | 1.421087 | 0.586131 | 0.294935 | 0.0 | 4.891715 | -1.554233 | -inf | 0.004845 | 2740.425311 |
| 3 | 8.1 | 0.25 | 0.847 | 0.8 | -2.172126 | -0.459132 | 4.753958 | 3.448162 | 3.523419 | 3.426509 | ... | 3.872527 | 1.393568 | 0.587446 | 0.293379 | 0.0 | 4.753352 | -1.426080 | -inf | 0.006728 | 2806.479796 |
| 4 | 8.1 | 0.30 | 0.847 | 0.8 | -2.049000 | -0.416230 | 4.747312 | 3.457492 | 3.532750 | 3.435840 | ... | 3.733294 | 1.366071 | 0.588768 | 0.290341 | 0.0 | 4.612402 | -1.356427 | -inf | 0.008933 | 2867.426272 |
| 5 | 8.1 | 0.35 | 0.847 | 0.8 | -1.931932 | -0.376595 | 4.734990 | 3.466942 | 3.542199 | 3.445289 | ... | 3.600167 | 1.338875 | 0.589996 | 0.286253 | 0.0 | 4.476416 | -1.302754 | -inf | 0.011697 | 2930.501311 |
| 6 | 8.1 | 0.40 | 0.847 | 0.8 | -1.809499 | -0.338319 | 4.716429 | 3.478412 | 3.553670 | 3.456760 | ... | 3.439087 | 1.303660 | 0.591724 | 0.280542 | 0.0 | 4.311352 | -1.260680 | -inf | 0.015506 | 3008.929900 |
| 7 | 8.1 | 0.45 | 0.847 | 0.8 | -1.671891 | -0.297670 | 4.686284 | 3.492490 | 3.567747 | 3.470837 | ... | 3.254961 | 1.260181 | 0.594213 | 0.271423 | 0.0 | 4.120597 | -1.195125 | -inf | 0.021287 | 3108.062046 |
| 8 | 8.1 | 0.50 | 0.847 | 0.8 | -1.501147 | -0.253464 | 4.643629 | 3.513072 | 3.588330 | 3.491420 | ... | 2.995661 | 1.192625 | 0.601096 | 0.255155 | 0.0 | 3.851911 | -0.991065 | 0.048188 | 0.031539 | 3258.910549 |
| 9 | 8.1 | 0.55 | 0.847 | 0.8 | -1.289990 | -0.212640 | 4.603374 | 3.545450 | 3.620707 | 3.523797 | ... | 2.677116 | 1.102191 | 0.622722 | 0.229291 | 0.0 | 3.529129 | -0.728217 | 1.942069 | 0.051287 | 3511.151994 |
| 10 | 8.1 | 0.60 | 0.847 | 0.8 | -1.103566 | -0.197423 | 4.610728 | 3.584447 | 3.659705 | 3.562795 | ... | 2.323397 | 0.990639 | 0.643738 | 0.207503 | 0.0 | 3.174638 | -0.534171 | 2.641929 | 0.078783 | 3841.026027 |
| 11 | 8.1 | 0.65 | 0.847 | 0.8 | -1.035662 | -0.190024 | 4.630692 | 3.597724 | 3.672981 | 3.576071 | ... | 2.206161 | 0.951923 | 0.648395 | 0.196717 | 0.0 | 3.051273 | -0.470558 | 2.935574 | 0.092117 | 3960.258456 |
| 12 | 8.1 | 0.70 | 0.847 | 0.8 | -0.893310 | -0.162198 | 4.607226 | 3.619399 | 3.694656 | 3.597746 | ... | 2.039102 | 0.893324 | 0.663340 | 0.168677 | 0.0 | 2.871119 | -0.380797 | 3.080684 | 0.127847 | 4162.927620 |
| 13 | 8.1 | 0.75 | 0.847 | 0.8 | -0.749999 | -0.133807 | 4.580406 | 3.641031 | 3.716288 | 3.619378 | ... | 1.891602 | 0.837590 | 0.663877 | 0.137911 | 0.0 | 2.693391 | -0.337469 | 3.157947 | 0.177828 | 4375.532359 |
| 14 | 8.1 | 0.80 | 0.847 | 0.8 | -0.619289 | -0.104438 | 4.549698 | 3.659024 | 3.734282 | 3.637372 | ... | 1.761485 | 0.785528 | 0.648344 | 0.113136 | 0.0 | 2.522965 | -0.304589 | 3.201491 | 0.240276 | 4560.623177 |
| 15 | 8.1 | 0.85 | 0.847 | 0.8 | -0.494575 | -0.073767 | 4.514684 | 3.674867 | 3.750125 | 3.653215 | ... | 1.643328 | 0.739492 | 0.612932 | 0.098517 | 0.0 | 2.354777 | -0.292595 | 3.225745 | 0.320202 | 4730.064360 |
| 16 | 8.1 | 0.90 | 0.847 | 0.8 | -0.375724 | -0.041868 | 4.475710 | 3.688631 | 3.763888 | 3.666978 | ... | 1.544597 | 0.701406 | 0.568451 | 0.089794 | 0.0 | 2.202842 | -0.267726 | 3.239739 | 0.420995 | 4882.368444 |
| 17 | 8.1 | 0.95 | 0.847 | 0.8 | -0.261217 | -0.009255 | 4.433965 | 3.700951 | 3.776208 | 3.679298 | ... | 1.460212 | 0.668364 | 0.525453 | 0.083907 | 0.0 | 2.069572 | -0.246427 | 3.252158 | 0.548003 | 5022.854178 |
| 18 | 8.1 | 1.00 | 0.847 | 0.8 | -0.150509 | 0.023071 | 4.391589 | 3.712464 | 3.787722 | 3.690812 | ... | 1.388522 | 0.639261 | 0.491256 | 0.077892 | 0.0 | 1.957670 | -0.237135 | 3.263712 | 0.707116 | 5157.797769 |
| 19 | 8.1 | 1.05 | 0.847 | 0.8 | -0.044366 | 0.054240 | 4.350440 | 3.723416 | 3.798673 | 3.701763 | ... | 1.321207 | 0.612244 | 0.458590 | 0.072423 | 0.0 | 1.852219 | -0.227984 | 3.273507 | 0.902888 | 5289.511674 |
| 20 | 8.1 | 1.10 | 0.847 | 0.8 | 0.056709 | 0.083544 | 4.312036 | 3.734032 | 3.809290 | 3.712380 | ... | 1.258516 | 0.587548 | 0.427405 | 0.068161 | 0.0 | 1.754081 | -0.213987 | 3.280966 | 1.139485 | 5420.412773 |
| 21 | 8.1 | 1.15 | 0.847 | 0.8 | 0.152666 | 0.109666 | 4.279098 | 3.744961 | 3.820219 | 3.723309 | ... | 1.196089 | 0.563002 | 0.397408 | 0.063248 | 0.0 | 1.656745 | -0.207143 | 3.286534 | 1.421237 | 5558.543901 |
| 22 | 8.1 | 1.20 | 0.847 | 0.8 | 0.243698 | 0.131549 | 4.253815 | 3.756777 | 3.832035 | 3.735125 | ... | 1.130216 | 0.537072 | 0.366100 | 0.058241 | 0.0 | 1.554558 | -0.198745 | 3.290561 | 1.752660 | 5711.855868 |
| 23 | 8.1 | 1.25 | 0.847 | 0.8 | 0.330066 | 0.148334 | 4.237973 | 3.769977 | 3.845234 | 3.748324 | ... | 1.059353 | 0.508874 | 0.333063 | 0.053028 | 0.0 | 1.445444 | -0.187981 | 3.293454 | 2.138289 | 5888.121595 |
| 24 | 8.1 | 1.30 | 0.847 | 0.8 | 0.412044 | 0.152254 | 4.247167 | 3.788511 | 3.863769 | 3.766859 | ... | 0.962256 | 0.469227 | 0.289012 | 0.046617 | 0.0 | 1.297885 | -0.164622 | 3.295477 | 2.582520 | 6144.850624 |
25 rows × 60 columns
SPOTS_edr3['34'][nearest_age]
| logAge | Mass | Fspot | Xspot | log(L/Lsun) | log(R/Rsun) | log(g) | log(Teff) | log(T_hot) | log(T_cool) | ... | G-J | G-RP | J-H | H-K | G-H | G-K | G-V | A(Li) | Lsun | Teff | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 8.1 | 0.10 | 0.339 | 0.8 | -2.712418 | -0.730690 | 4.899133 | 3.448868 | 3.473095 | 3.376185 | ... | 3.742510 | 1.367287 | 0.592289 | 0.279062 | 0.0 | 4.613860 | -2.225280 | -inf | 0.001939 | 2811.043719 |
| 1 | 8.1 | 0.15 | 0.339 | 0.8 | -2.429194 | -0.639035 | 4.891914 | 3.473846 | 3.498074 | 3.401164 | ... | 3.552493 | 1.303907 | 0.593885 | 0.273108 | 0.0 | 4.419486 | -1.452944 | -inf | 0.003722 | 2977.461880 |
| 2 | 8.1 | 0.20 | 0.339 | 0.8 | -2.239438 | -0.570778 | 4.880339 | 3.487157 | 3.511384 | 3.414474 | ... | 3.449597 | 1.270577 | 0.595468 | 0.270133 | 0.0 | 4.315199 | -1.144710 | -inf | 0.005762 | 3070.130315 |
| 3 | 8.1 | 0.25 | 0.339 | 0.8 | -2.093052 | -0.516734 | 4.869161 | 3.496731 | 3.520959 | 3.424049 | ... | 3.346588 | 1.245101 | 0.597407 | 0.268085 | 0.0 | 4.212080 | -1.002123 | -inf | 0.008071 | 3138.565164 |
| 4 | 8.1 | 0.30 | 0.339 | 0.8 | -1.970007 | -0.470971 | 4.856795 | 3.504611 | 3.528839 | 3.431929 | ... | 3.251364 | 1.223920 | 0.599437 | 0.266240 | 0.0 | 4.117042 | -0.921175 | -inf | 0.010715 | 3196.032449 |
| 5 | 8.1 | 0.35 | 0.339 | 0.8 | -1.855306 | -0.429144 | 4.840087 | 3.512373 | 3.536600 | 3.439690 | ... | 3.164504 | 1.204025 | 0.601326 | 0.263619 | 0.0 | 4.029448 | -0.853239 | -inf | 0.013954 | 3253.664509 |
| 6 | 8.1 | 0.40 | 0.339 | 0.8 | -1.732767 | -0.389883 | 4.819557 | 3.523377 | 3.547604 | 3.450694 | ... | 3.043895 | 1.175900 | 0.604096 | 0.259253 | 0.0 | 3.907245 | -0.775352 | -inf | 0.018503 | 3337.158468 |
| 7 | 8.1 | 0.45 | 0.339 | 0.8 | -1.598062 | -0.348402 | 4.787747 | 3.536313 | 3.560540 | 3.463630 | ... | 2.903786 | 1.141178 | 0.607729 | 0.253016 | 0.0 | 3.764531 | -0.716838 | -inf | 0.025231 | 3438.053813 |
| 8 | 8.1 | 0.50 | 0.339 | 0.8 | -1.440761 | -0.304085 | 4.744871 | 3.553480 | 3.577707 | 3.480797 | ... | 2.725873 | 1.093817 | 0.613757 | 0.240962 | 0.0 | 3.580592 | -0.606917 | -inf | 0.036244 | 3576.675831 |
| 9 | 8.1 | 0.55 | 0.339 | 0.8 | -1.270122 | -0.264206 | 4.706506 | 3.576200 | 3.600427 | 3.503517 | ... | 2.512445 | 1.032694 | 0.626376 | 0.218399 | 0.0 | 3.357220 | -0.474141 | -inf | 0.053688 | 3768.772046 |
| 10 | 8.1 | 0.60 | 0.339 | 0.8 | -1.153239 | -0.239203 | 4.694288 | 3.592919 | 3.617146 | 3.520236 | ... | 2.374117 | 0.990087 | 0.633704 | 0.201647 | 0.0 | 3.209468 | -0.431123 | -1.097940 | 0.070269 | 3916.686310 |
| 11 | 8.1 | 0.65 | 0.339 | 0.8 | -1.031144 | -0.210744 | 4.672133 | 3.609213 | 3.633441 | 3.536531 | ... | 2.241838 | 0.946093 | 0.637087 | 0.188190 | 0.0 | 3.067116 | -0.360417 | 0.878639 | 0.093080 | 4066.430555 |
| 12 | 8.1 | 0.70 | 0.339 | 0.8 | -0.876848 | -0.181250 | 4.645329 | 3.633040 | 3.657268 | 3.560358 | ... | 2.047628 | 0.878765 | 0.628806 | 0.172535 | 0.0 | 2.848968 | -0.299951 | 1.935785 | 0.132786 | 4295.761882 |
| 13 | 8.1 | 0.75 | 0.339 | 0.8 | -0.736951 | -0.157424 | 4.627641 | 3.656102 | 3.680329 | 3.583419 | ... | 1.868222 | 0.815837 | 0.611799 | 0.155998 | 0.0 | 2.636019 | -0.230109 | 2.463906 | 0.183252 | 4530.035861 |
| 14 | 8.1 | 0.80 | 0.339 | 0.8 | -0.605662 | -0.132469 | 4.605759 | 3.676446 | 3.700674 | 3.603764 | ... | 1.731855 | 0.765344 | 0.601383 | 0.134222 | 0.0 | 2.467459 | -0.192590 | 2.756017 | 0.247935 | 4747.295001 |
| 15 | 8.1 | 0.85 | 0.339 | 0.8 | -0.481992 | -0.107274 | 4.581698 | 3.694766 | 3.718994 | 3.622084 | ... | 1.618071 | 0.721858 | 0.583690 | 0.114929 | 0.0 | 2.316690 | -0.163635 | 2.929287 | 0.329615 | 4951.834257 |
| 16 | 8.1 | 0.90 | 0.339 | 0.8 | -0.364868 | -0.081453 | 4.554880 | 3.711137 | 3.735364 | 3.638454 | ... | 1.514684 | 0.681663 | 0.561779 | 0.097240 | 0.0 | 2.173704 | -0.159854 | 3.039105 | 0.431650 | 5142.056686 |
| 17 | 8.1 | 0.95 | 0.339 | 0.8 | -0.252749 | -0.054619 | 4.524694 | 3.725750 | 3.749977 | 3.653067 | ... | 1.421049 | 0.645613 | 0.528213 | 0.086146 | 0.0 | 2.035408 | -0.159072 | 3.110233 | 0.558793 | 5318.017351 |
| 18 | 8.1 | 1.00 | 0.339 | 0.8 | -0.144951 | -0.026781 | 4.491293 | 3.738780 | 3.763008 | 3.666098 | ... | 1.341370 | 0.615109 | 0.490456 | 0.078869 | 0.0 | 1.910695 | -0.147280 | 3.159421 | 0.716224 | 5479.993836 |
| 19 | 8.1 | 1.05 | 0.339 | 0.8 | -0.041363 | 0.001673 | 4.455574 | 3.750450 | 3.774677 | 3.677767 | ... | 1.270744 | 0.587172 | 0.454556 | 0.073601 | 0.0 | 1.798901 | -0.133883 | 3.194028 | 0.909154 | 5629.242268 |
| 20 | 8.1 | 1.10 | 0.339 | 0.8 | 0.058102 | 0.029655 | 4.419813 | 3.761325 | 3.785553 | 3.688643 | ... | 1.208803 | 0.561706 | 0.424804 | 0.068538 | 0.0 | 1.702145 | -0.137937 | 3.219617 | 1.143146 | 5771.983488 |
| 21 | 8.1 | 1.15 | 0.339 | 0.8 | 0.153256 | 0.056658 | 4.385114 | 3.771612 | 3.795840 | 3.698930 | ... | 1.151474 | 0.538113 | 0.397171 | 0.063878 | 0.0 | 1.612523 | -0.141929 | 3.239359 | 1.423167 | 5910.339933 |
| 22 | 8.1 | 1.20 | 0.339 | 0.8 | 0.243977 | 0.082324 | 4.352265 | 3.781460 | 3.805687 | 3.708777 | ... | 1.097759 | 0.516154 | 0.370454 | 0.060524 | 0.0 | 1.528737 | -0.142472 | 3.255346 | 1.753789 | 6045.883439 |
| 23 | 8.1 | 1.25 | 0.339 | 0.8 | 0.330256 | 0.105689 | 4.323264 | 3.791347 | 3.815575 | 3.718665 | ... | 1.044562 | 0.494133 | 0.345278 | 0.056602 | 0.0 | 1.446442 | -0.141702 | 3.267539 | 2.139224 | 6185.104762 |
| 24 | 8.1 | 1.30 | 0.339 | 0.8 | 0.412254 | 0.125899 | 4.299877 | 3.801741 | 3.825969 | 3.729059 | ... | 0.990369 | 0.471804 | 0.319197 | 0.053157 | 0.0 | 1.362724 | -0.139495 | 3.276616 | 2.583771 | 6334.923139 |
25 rows × 60 columns
spots_f000_edr3.columns
Index(['logAge', 'Mass', 'Fspot', 'Xspot', 'log(L/Lsun)', 'log(R/Rsun)',
'log(g)', 'log(Teff)', 'log(T_hot)', 'log(T_cool)', 'TauCZ', 'Li/Li0',
'B_mag', 'V_mag', 'Rc_mag', 'Ic_mag', 'J_mag', 'H_mag', 'K_mag',
'W1_mag', 'G_mag', 'BP_mag', 'RP_mag', 'Thot', 'Tcool', 'Label_0',
'J_hot', 'H_hot', 'Ks_hot', 'Label_1', 'J_cool', 'H_cool', 'Ks_cool',
'Label_2', 'G_hot', 'G_BP_hot', 'G_RP_hot', 'Label_3', 'G_cool',
'G_BP_cool', 'G_RP_cool'],
dtype='object')
#for age in SPOTS_edr3['00'].keys():
# print(age)
# print(min(SPOTS_edr3['00'][age]['Teff']))
for f in SPOTS_edr3.keys():
print(f)
00 17 34 51 68 85
SPOTS_edr3['00'].keys()
dict_keys([0.001, 0.002, 0.003, 0.004, 0.005, 0.006, 0.007, 0.008, 0.009, 0.01, 0.011, 0.013, 0.014, 0.016, 0.018, 0.02, 0.022, 0.025, 0.028, 0.032, 0.035, 0.04, 0.045, 0.05, 0.056, 0.063, 0.071, 0.079, 0.089, 0.1, 0.112, 0.126, 0.141, 0.158, 0.178, 0.2, 0.224, 0.251, 0.282, 0.316, 0.355, 0.398, 0.447, 0.501, 0.562, 0.631, 0.708, 0.794, 0.891, 1.0, 1.122, 1.259, 1.413, 1.585, 1.778, 1.995, 2.239, 2.512, 2.818, 3.162, 3.548, 3.981])
SPOTS_edr3['00'][nearest_age].columns
Index(['logAge', 'Mass', 'Fspot', 'Xspot', 'log(L/Lsun)', 'log(R/Rsun)',
'log(g)', 'log(Teff)', 'log(T_hot)', 'log(T_cool)', 'TauCZ', 'Li/Li0',
'B_mag', 'V_mag', 'Rc_mag', 'Ic_mag', 'J_mag', 'H_mag', 'K_mag',
'W1_mag', 'G_mag', 'BP_mag', 'RP_mag', 'Thot', 'Tcool', 'Label_0',
'J_hot', 'H_hot', 'Ks_hot', 'Label_1', 'J_cool', 'H_cool', 'Ks_cool',
'Label_2', 'G_hot', 'G_BP_hot', 'G_RP_hot', 'Label_3', 'G_cool',
'G_BP_cool', 'G_RP_cool', 'Age_Gyr', 'BP_abs', 'RP_abs', 'G_abs',
'J_abs', 'H_abs', 'K_abs', 'M/Ms', 'BP-RP', 'G-J', 'G-RP', 'J-H', 'H-K',
'G-H', 'G-K', 'G-V', 'A(Li)', 'Lsun', 'Teff'],
dtype='object')
fig, ax = plt.subplots(1, 1, figsize=(8, 6))
ax.set_ylabel('$A(Li)$ [dex]')
ax.set_xlabel('$B_p-R_p$ [mag]')
ax.plot(SPOTS_edr3['00'][0.025]['BP-RP'], SPOTS_edr3['00'][0.025]['A(Li)'], linewidth=1, label='SPOTS-YBC f000; 25 Myr')
ax.plot(SPOTS_edr3['17'][0.006]['BP-RP'], SPOTS_edr3['17'][0.006]['A(Li)'], linewidth=1, label='SPOTS-YBC f017; 6 Myr')
ax.plot(SPOTS_edr3['34'][0.010]['BP-RP'], SPOTS_edr3['34'][0.010]['A(Li)'], linewidth=1, label='SPOTS-YBC f034; 10 Myr')
ax.plot(SPOTS_edr3['51'][0.016]['BP-RP'], SPOTS_edr3['51'][0.016]['A(Li)'], linewidth=1, label='SPOTS-YBC f051; 16 Myr')
ax.plot(SPOTS_edr3['68'][0.028]['BP-RP'], SPOTS_edr3['68'][0.028]['A(Li)'], linewidth=1, label='SPOTS-YBC f068; 28 Myr')
ax.plot(SPOTS_edr3['85'][0.035]['BP-RP'], SPOTS_edr3['85'][0.035]['A(Li)'], linewidth=1, label='SPOTS-YBC f085; 35 Myr')
ax.plot(SPOTS_edr3['00'][0.126]['BP-RP'], SPOTS_edr3['00'][0.126]['A(Li)'], linewidth=1, color = 'k', linestyle='--', label='SPOTS-YBC f000; 126 Myr')
ax.errorbar(data_obs_Pleiades['bp']-data_obs_Pleiades['rp'], data_obs_Pleiades['ALi'], yerr=data_obs_Pleiades['e_ALi'], fmt='.', zorder=0, color='r', elinewidth=1, capsize=2)
ax.legend(fontsize=12, loc='upper center', bbox_to_anchor=(1.25, 0.9))
<matplotlib.legend.Legend at 0x7fd726796ad0>
fig, ax = plt.subplots(1, 1, figsize=(8, 6))
ax.set_ylabel('$A(Li)$ [dex]')
ax.set_xlabel('$G-J$ [mag]')
ax.plot(SPOTS_edr3['00'][0.025]['G-J'], SPOTS_edr3['00'][0.025]['A(Li)'], linewidth=1, label='SPOTS-YBC f000; 25 Myr')
ax.plot(SPOTS_edr3['17'][0.006]['G-J'], SPOTS_edr3['17'][0.006]['A(Li)'], linewidth=1, label='SPOTS-YBC f017; 6 Myr')
ax.plot(SPOTS_edr3['34'][0.010]['G-J'], SPOTS_edr3['34'][0.010]['A(Li)'], linewidth=1, label='SPOTS-YBC f034; 10 Myr')
ax.plot(SPOTS_edr3['51'][0.016]['G-J'], SPOTS_edr3['51'][0.016]['A(Li)'], linewidth=1, label='SPOTS-YBC f051; 16 Myr')
ax.plot(SPOTS_edr3['68'][0.028]['G-J'], SPOTS_edr3['68'][0.028]['A(Li)'], linewidth=1, label='SPOTS-YBC f068; 28 Myr')
ax.plot(SPOTS_edr3['85'][0.035]['G-J'], SPOTS_edr3['85'][0.035]['A(Li)'], linewidth=1, label='SPOTS-YBC f085; 35 Myr')
ax.plot(SPOTS_edr3['00'][0.126]['G-J'], SPOTS_edr3['00'][0.126]['A(Li)'], linewidth=1, color = 'k', linestyle='--', label='SPOTS-YBC f000; 126 Myr')
ax.plot(SPOTS_edr3['85'][0.126]['G-J'], SPOTS_edr3['85'][0.126]['A(Li)'], linewidth=1, color = 'k', linestyle=':', label='SPOTS-YBC f085; 126 Myr')
ax.errorbar(data_obs_Pleiades['g']-data_obs_Pleiades['Jmag'], data_obs_Pleiades['ALi'], yerr=data_obs_Pleiades['e_ALi'], fmt='.', zorder=0, color='r', elinewidth=1, capsize=2)
ax.legend(fontsize=12, loc='upper center', bbox_to_anchor=(1.25, 0.9))
<matplotlib.legend.Legend at 0x7fd7228a20d0>
fig, ax = plt.subplots(1, 1, figsize=(8, 6))
ax.set_ylabel('$A(Li)$ [dex]')
ax.set_xlabel('$T_{eff}$ [K]')
ax.plot(SPOTS_edr3['00'][0.056]['Teff'], SPOTS_edr3['00'][0.056]['A(Li)'], linewidth=1, color='orange', label='SPOTS-YBC f000; 56 Myr')
ax.plot(SPOTS_edr3['17'][0.056]['Teff'], SPOTS_edr3['17'][0.056]['A(Li)'], linewidth=1, linestyle='--', color='orange', label='SPOTS-YBC f017; 56 Myr')
ax.plot(SPOTS_edr3['85'][0.056]['Teff'], SPOTS_edr3['51'][0.056]['A(Li)'], linewidth=1, linestyle=':', color='orange', label='SPOTS-YBC f085; 50 Myr')
ax.plot(SPOTS_edr3['00'][0.05]['Teff'], SPOTS_edr3['00'][0.05]['A(Li)'], linewidth=1, color='r', label='SPOTS-YBC f000; 50 Myr')
ax.plot(SPOTS_edr3['17'][0.05]['Teff'], SPOTS_edr3['17'][0.05]['A(Li)'], linewidth=1, linestyle='--', color='r', label='SPOTS-YBC f017; 50 Myr')
ax.plot(SPOTS_edr3['85'][0.05]['Teff'], SPOTS_edr3['51'][0.05]['A(Li)'], linewidth=1, linestyle=':', color='r', label='SPOTS-YBC f085; 50 Myr')
ax.plot(SPOTS_edr3['00'][0.126]['Teff'], SPOTS_edr3['00'][0.126]['A(Li)'], linewidth=1, color = 'k', label='SPOTS-YBC f000; 126 Myr')
ax.plot(SPOTS_edr3['17'][0.126]['Teff'], SPOTS_edr3['17'][0.126]['A(Li)'], linewidth=1, color = 'k', linestyle='--', label='SPOTS-YBC f017; 126 Myr')
ax.plot(SPOTS_edr3['85'][0.126]['Teff'], SPOTS_edr3['85'][0.126]['A(Li)'], linewidth=1, color = 'k', linestyle=':', label='SPOTS-YBC f085; 126 Myr')
ax.invert_xaxis()
ax.errorbar(data_obs_Pleiades['Teff'], data_obs_Pleiades['ALi'], yerr=data_obs_Pleiades['e_ALi'], fmt='.', zorder=0, color='r', elinewidth=1, capsize=2)
ax.legend(fontsize=12, loc='upper center', bbox_to_anchor=(1.25, 0.9))
<matplotlib.legend.Legend at 0x7fd72d45a1d0>
fig, ax = plt.subplots(1, 1, figsize=(8, 6))
ax.set_ylabel('$G$ [mag]')
ax.set_xlabel('$B_p-R_p$ [mag]')
ax.plot(SPOTS_edr3['00'][0.126]['BP_abs']-SPOTS_edr3['00'][0.126]['RP_abs'], SPOTS_edr3['00'][0.126]['G_abs'], linewidth=1, label='SPOTS-YBC f000; 126 Myr', color='k')
ax.plot(SPOTS_edr3['17'][0.126]['BP_abs']-SPOTS_edr3['17'][0.126]['RP_abs'], SPOTS_edr3['17'][0.126]['G_abs'], linewidth=1, label='SPOTS-YBC f017; 126 Myr', color='r')
ax.plot(SPOTS_edr3['34'][0.126]['BP_abs']-SPOTS_edr3['34'][0.126]['RP_abs'], SPOTS_edr3['34'][0.126]['G_abs'], linewidth=1, label='SPOTS-YBC f034; 126 Myr', color='orange')
ax.plot(SPOTS_edr3['51'][0.126]['BP_abs']-SPOTS_edr3['51'][0.126]['RP_abs'], SPOTS_edr3['51'][0.126]['G_abs'], linewidth=1, label='SPOTS-YBC f051; 126 Myr', color='green')
ax.plot(SPOTS_edr3['85'][0.126]['BP_abs']-SPOTS_edr3['85'][0.126]['RP_abs'], SPOTS_edr3['85'][0.126]['G_abs'], linewidth=1, label='SPOTS-YBC f085; 126 Myr', color='blue')
ax.errorbar(data_obs_Pleiades['BP_abs']-data_obs_Pleiades['RP_abs'], data_obs_Pleiades['G_abs'], yerr=data_obs_Pleiades['e_g'], fmt='.', zorder=0, color='r', elinewidth=1, capsize=2, alpha=0.125)
ax.legend(fontsize=12, loc='upper center', bbox_to_anchor=(1.25, 0.9))
ax.invert_yaxis()
fig, ax = plt.subplots(1, 1, figsize=(8, 6))
ax.set_ylabel('$G$ [mag]')
ax.set_xlabel('$G-J$ [mag]')
ax.plot(SPOTS_edr3['00'][0.126]['G_abs']-SPOTS_edr3['00'][0.126]['J_abs'], SPOTS_edr3['00'][0.126]['G_abs'], linewidth=1, label='SPOTS-YBC f000; 126 Myr', color='k')
ax.plot(SPOTS_edr3['17'][0.126]['G_abs']-SPOTS_edr3['17'][0.126]['J_abs'], SPOTS_edr3['17'][0.126]['G_abs'], linewidth=1, label='SPOTS-YBC f017; 126 Myr', color='r')
ax.plot(SPOTS_edr3['34'][0.126]['G_abs']-SPOTS_edr3['34'][0.126]['J_abs'], SPOTS_edr3['34'][0.126]['G_abs'], linewidth=1, label='SPOTS-YBC f034; 126 Myr', color='orange')
ax.plot(SPOTS_edr3['51'][0.126]['G_abs']-SPOTS_edr3['51'][0.126]['J_abs'], SPOTS_edr3['51'][0.126]['G_abs'], linewidth=1, label='SPOTS-YBC f051; 126 Myr', color='green')
ax.plot(SPOTS_edr3['85'][0.126]['G_abs']-SPOTS_edr3['85'][0.126]['J_abs'], SPOTS_edr3['85'][0.126]['G_abs'], linewidth=1, label='SPOTS-YBC f085; 126 Myr', color='blue')
ax.errorbar(data_obs_Pleiades['G_abs']-data_obs_Pleiades['J_abs'], data_obs_Pleiades['G_abs'], yerr=data_obs_Pleiades['e_g'], fmt='.', zorder=0, color='r', elinewidth=1, capsize=2, alpha=0.125)
ax.legend(fontsize=12, loc='upper center', bbox_to_anchor=(1.25, 0.9))
ax.invert_yaxis()
fig, ax = plt.subplots(1, 1, figsize=(8, 6))
ax.set_ylabel('$G$ [mag]')
ax.set_xlabel('$G-R_p$ [mag]')
ax.plot(SPOTS_edr3['00'][0.126]['G_abs']-SPOTS_edr3['00'][0.126]['RP_abs'], SPOTS_edr3['00'][0.126]['G_abs'], linewidth=1, label='SPOTS-YBC f000; 126 Myr', color='k')
ax.plot(SPOTS_edr3['17'][0.126]['G_abs']-SPOTS_edr3['17'][0.126]['RP_abs'], SPOTS_edr3['17'][0.126]['G_abs'], linewidth=1, label='SPOTS-YBC f017; 126 Myr', color='r')
ax.plot(SPOTS_edr3['34'][0.126]['G_abs']-SPOTS_edr3['34'][0.126]['RP_abs'], SPOTS_edr3['34'][0.126]['G_abs'], linewidth=1, label='SPOTS-YBC f034; 126 Myr', color='orange')
ax.plot(SPOTS_edr3['51'][0.126]['G_abs']-SPOTS_edr3['51'][0.126]['RP_abs'], SPOTS_edr3['51'][0.126]['G_abs'], linewidth=1, label='SPOTS-YBC f051; 126 Myr', color='green')
ax.plot(SPOTS_edr3['85'][0.126]['G_abs']-SPOTS_edr3['85'][0.126]['RP_abs'], SPOTS_edr3['85'][0.126]['G_abs'], linewidth=1, label='SPOTS-YBC f085; 126 Myr', color='blue')
ax.errorbar(data_obs_Pleiades['G_abs']-data_obs_Pleiades['RP_abs'], data_obs_Pleiades['G_abs'], yerr=data_obs_Pleiades['e_g'], fmt='.', zorder=0, color='r', elinewidth=1, capsize=2, alpha=0.125)
ax.legend(fontsize=12, loc='upper center', bbox_to_anchor=(1.25, 0.9))
ax.invert_yaxis()
ax.set_xlim(0, 1.5)
(0.0, 1.5)
SPOTS_edr3['00'][0.126]['Teff']
0 2949.742462 1 3141.861887 2 3243.838470 3 3317.959343 4 3378.171160 5 3436.631053 6 3518.427448 7 3615.667953 8 3741.906660 9 3891.281506 10 4014.089117 11 4183.549674 12 4395.352697 13 4615.127249 14 4839.938464 15 5056.377615 16 5261.455359 17 5454.735418 18 5635.369148 19 5801.018360 20 5953.197490 21 6098.292624 22 6238.221627 23 6374.414055 24 6512.426278 Name: Teff, dtype: float64
fig, ax = plt.subplots(1, 1, figsize=(8, 6))
ax.set_ylabel('$\log{(\mathcal{L}/\mathcal{L}_{\odot})}$')
ax.set_xlabel('$T_{eff}$ [K]')
ax.plot(SPOTS_edr3['00'][0.126]['Teff'], SPOTS_edr3['00'][0.126]['log(L/Lsun)'], linewidth=1, label='SPOTS-YBC f000; 126 Myr', color='k')
ax.plot(SPOTS_edr3['17'][0.126]['Teff'], SPOTS_edr3['17'][0.126]['log(L/Lsun)'], linewidth=1, label='SPOTS-YBC f017; 126 Myr', color='r')
ax.plot(SPOTS_edr3['34'][0.126]['Teff'], SPOTS_edr3['34'][0.126]['log(L/Lsun)'], linewidth=1, label='SPOTS-YBC f034; 126 Myr', color='orange')
ax.plot(SPOTS_edr3['51'][0.126]['Teff'], SPOTS_edr3['51'][0.126]['log(L/Lsun)'], linewidth=1, label='SPOTS-YBC f051; 126 Myr', color='green')
ax.plot(SPOTS_edr3['85'][0.126]['Teff'], SPOTS_edr3['85'][0.126]['log(L/Lsun)'], linewidth=1, label='SPOTS-YBC f085; 126 Myr', color='blue')
ax.scatter(data_obs_Pleiades['Teff_x'], data_obs_Pleiades['log(L/Lsun)'], zorder=0, color='r', s=10, alpha=0.125)
ax.legend(fontsize=12, loc='upper center', bbox_to_anchor=(1.25, 0.9))
ax.invert_xaxis()
from models_test import PlotAnalyzer
BTSettl_Li_isochrones_Teff.columns
Index(['age_Gyr', 't(Gyr)', 'M/Ms', 'Teff', 'log(L/Lsun)', 'lg(g)', 'R(Gcm)',
'D', 'Li', 'G_abs', 'BP_abs', 'RP_abs', 'J_abs', 'H_abs', 'K_abs',
'g_abs', 'r_abs', 'i_abs', 'y_abs', 'z_abs', 'A(Li)', 'Lsun'],
dtype='object')
plot_analyzer = PlotAnalyzer(path_all)
SPOTS = plot_analyzer.SPOTS_edr3
data_obs = data_obs_Pleiades
band_1 = 'BP_abs'
band_2 = 'RP_abs'
band_y = 'G_abs'
age_iso = 0.120
max_mag = 3.5
intervals, interval_x, x, y, e_x, e_y = plot_analyzer.plot_process_CMD(SPOTS, data_obs, band_1, band_2, band_y, age_iso, max_mag)
SPOTS_iso = plot_analyzer.plot_result(interval_x, x, y, e_x, e_y, SPOTS, band_1, band_2, band_y, data_obs, age_iso, max_mag, l=2, BTSettl=True)
Exe. time: 7 minutos y 31.24 segundos. Exe. time: 0 minutos y 0.07 segundos.
plot_analyzer = PlotAnalyzer(path_all)
SPOTS = plot_analyzer.SPOTS_edr3
data_obs = data_obs_Pleiades
band_1 = 'G_abs'
band_2 = 'J_abs'
band_y = 'G_abs'
age_iso = 0.120
max_mag = 3.5
intervals_J, interval_x_J, x_J, y_J, e_x_J, e_y_J = plot_analyzer.plot_process_CMD(SPOTS, data_obs, band_1, band_2, band_y, age_iso, max_mag)
SPOTS_iso_J = plot_analyzer.plot_result(interval_x_J, x_J, y_J, e_x_J, e_y_J, SPOTS, band_1, band_2, band_y, data_obs, age_iso, max_mag, l=2, BTSettl=True)
Exe. time: 7 minutos y 12.63 segundos. Exe. time: 0 minutos y 0.07 segundos.
plot_analyzer = PlotAnalyzer(path_all)
SPOTS = plot_analyzer.SPOTS_edr3
data_obs = data_obs_Pleiades
band_1 = 'G_abs'
band_2 = 'RP_abs'
band_y = 'G_abs'
age_iso = 0.120
max_mag = 1.4
intervals_R, interval_x_R, x_R, y_R, e_x_R, e_y_R = plot_analyzer.plot_process_CMD(SPOTS, data_obs, band_1, band_2, band_y, age_iso, max_mag)
SPOTS_iso_R = plot_analyzer.plot_result(interval_x_R, x_R, y_R, e_x_R, e_y_R, SPOTS, band_1, band_2, band_y, data_obs, age_iso, max_mag, l=2, BTSettl=True)
Exe. time: 7 minutos y 43.65 segundos. Exe. time: 0 minutos y 0.09 segundos.
fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(16, 6))
ax1.set_ylabel('$G$ [mag]')
ax1.set_xlabel('$G-J$ [mag]')
ax1.plot(SPOTS_edr3['00'][0.126]['G-J'], SPOTS_edr3['00'][0.126]['G_abs'], label='SPOTS-YBC f000; 126 Myr', color='k', linewidth=1, linestyle='--')
ax1.plot(SPOTS_edr3['17'][0.126]['G-J'], SPOTS_edr3['17'][0.126]['G_abs'], label='SPOTS-YBC f017; 126 Myr', color='r', linewidth=1, linestyle='--')
ax1.plot(SPOTS_edr3['34'][0.126]['G-J'], SPOTS_edr3['34'][0.126]['G_abs'], label='SPOTS-YBC f034; 126 Myr', color='orange', linewidth=1, linestyle='--')
ax1.plot(SPOTS_edr3['51'][0.126]['G-J'], SPOTS_edr3['51'][0.126]['G_abs'], label='SPOTS-YBC f051; 126 Myr', color='green', linewidth=1, linestyle='--')
ax1.plot(SPOTS_edr3['51'][0.126]['G-J'], SPOTS_edr3['51'][0.126]['G_abs'], label='SPOTS-YBC f051; 126 Myr', color='blue', linewidth=1, linestyle='--')
ax1.plot(SPOTS_edr3['85'][0.126]['G-J'], SPOTS_edr3['85'][0.126]['G_abs'], label='SPOTS-YBC f085; 126 Myr', color='magenta', linewidth=1, linestyle='--')
ax1.errorbar(data_obs_Pleiades['G_abs']-data_obs_Pleiades['J_abs'], data_obs_Pleiades['G_abs'], yerr=data_obs_Pleiades['e_g'], fmt='.', zorder=0, color='r', elinewidth=1, capsize=2, alpha=0.125)
ax1.plot(SPOTS_iso_J[0.126]['G_abs']-SPOTS_iso_J[0.126]['J_abs'], SPOTS_iso_J[0.126]['G_abs'], linewidth=1, linestyle='-', color='r', label='Mixture Isochrone G-J', zorder=5)
ax1.plot(SPOTS_iso_R[0.126]['G_abs']-SPOTS_iso_R[0.126]['J_abs'], SPOTS_iso_R[0.126]['G_abs'], linewidth=1, linestyle='-', color='b', label='Mixture Isochrone BP-RP', zorder=5)
ax1.invert_yaxis()
ax2.set_ylabel('$G$ [mag]')
ax2.set_xlabel('$B_p-R_p$ [mag]')
ax2.plot(SPOTS_edr3['00'][0.126]['BP-RP'], SPOTS_edr3['00'][0.126]['G_abs'], color='k', linewidth=1, linestyle='--')
ax2.plot(SPOTS_edr3['17'][0.126]['BP-RP'], SPOTS_edr3['17'][0.126]['G_abs'], color='r', linewidth=1, linestyle='--')
ax2.plot(SPOTS_edr3['34'][0.126]['BP-RP'], SPOTS_edr3['34'][0.126]['G_abs'], color='orange', linewidth=1, linestyle='--')
ax2.plot(SPOTS_edr3['51'][0.126]['BP-RP'], SPOTS_edr3['51'][0.126]['G_abs'], color='green', linewidth=1, linestyle='--')
ax2.plot(SPOTS_edr3['51'][0.126]['BP-RP'], SPOTS_edr3['51'][0.126]['G_abs'], color='blue', linewidth=1, linestyle='--')
ax2.plot(SPOTS_edr3['85'][0.126]['BP-RP'], SPOTS_edr3['85'][0.126]['G_abs'], color='magenta', linewidth=1, linestyle='--')
ax2.errorbar(data_obs_Pleiades['BP_abs']-data_obs_Pleiades['RP_abs'], data_obs_Pleiades['G_abs'], yerr=data_obs_Pleiades['e_g'], fmt='.', zorder=0, color='r', elinewidth=1, capsize=2, alpha=0.125)
ax2.plot(SPOTS_iso[0.126]['BP_abs']-SPOTS_iso[0.126]['RP_abs'], SPOTS_iso[0.126]['G_abs'], linewidth=1, linestyle='-', color='b', label='Mixture Isochrone BP-RP', zorder=5)
ax2.plot(SPOTS_iso_R[0.126]['BP_abs']-SPOTS_iso_R[0.126]['RP_abs'], SPOTS_iso_R[0.126]['G_abs'], linewidth=1, linestyle='-', color='r', label='Mixture Isochrone G-RP', zorder=5)
ax2.invert_yaxis()
ax2.legend(fontsize=12, loc='upper center', bbox_to_anchor=(1.35, 0.9))
<matplotlib.legend.Legend at 0x7fe644f0e090>
for f in SPOTS_edr3.keys():
Teff_min = min(SPOTS_edr3[f][0.126]['Teff'])
print(Teff_min)
2949.742462182295 2883.9097147476045 2811.0437185962996 2730.020134059595 2639.0898719544107 2535.588947283602
min(data_obs_Pleiades['Teff'])
2500.0
min(data_obs_Pleiades['Teff_x'])
2937.2412
OPTION 2¶
from models_test import PlotAnalyzer
plot_analyzer = PlotAnalyzer(path_all)
SPOTS = plot_analyzer.SPOTS_edr3
data_obs = data_obs_Pleiades
age_iso = 0.120
intervals_Teff, interval_x_Teff, x_Teff, y_Teff, e_x_Teff, e_y_Teff = plot_analyzer.plot_process_HRD(SPOTS, data_obs, age_iso)
SPOTS_iso_Teff = plot_analyzer.plot_result_HRD(interval_x_Teff, x_Teff, y_Teff, e_x_Teff, e_y_Teff, SPOTS, data_obs, age_iso, l=0, BTSettl=True)
Exe. time: 0 minutos y 0.07 segundos.
plot_analyzer = PlotAnalyzer(path_all)
age_iso = 0.120
f_FGK = '17'
f_UCDs = '34'
mid = 12
plot_analyzer.plot_HRD(SPOTS_edr3, data_obs_Pleiades, age_iso, f_FGK, f_UCDs, mid)
{0.126: Teff log(L/Lsun)
0 6424.823035 0.412299
1 6281.771719 0.330340
2 6145.484816 0.244163
3 6008.130953 0.153698
4 5865.995426 0.059009
5 5719.419609 -0.039741
6 5562.186031 -0.142581
7 5390.253745 -0.249784
8 5204.909348 -0.361472
9 5006.433940 -0.478294
10 4794.969290 -0.601700
11 4530.035861 -0.736951
12 4295.761882 -0.876848
13 4066.430555 -1.031144
14 3916.686310 -1.153239
15 3768.772046 -1.270122
16 3576.675831 -1.440761
17 3438.053813 -1.598062
18 3337.158468 -1.732767
19 3253.664509 -1.855306
20 3196.032449 -1.970007
21 3138.565164 -2.093052
22 3070.130315 -2.239438
23 2977.461880 -2.429194}
Relate Teff to abundances and Teff to colores.
Teff, d, BC -> M_i (correct BT-Settl to fix SPOTS for low-mass stars)
SPOTS extension¶
from models_test import PlotAnalyzer
plt.rcParams.update({'font.size': 11, 'axes.linewidth': 1, 'axes.edgecolor': 'k'})
plt.rcParams['font.family'] = 'serif'
SPOTS = SPOTS_edr3
f = '00'
minim_age = 0.02
maxim_age = 0.120
upplim = 0.2
plot_analyzer = PlotAnalyzer(path_all)
plot_analyzer.plot_ages(SPOTS, f, minim_age, maxim_age, upplim, BTSettl_str=True)
SPOTS = SPOTS_edr3
f = '34'
minim_age = 0.02
maxim_age = 0.120
upplim = 0.2
plot_analyzer = PlotAnalyzer(path_all)
plot_analyzer.plot_ages(SPOTS, f, minim_age, maxim_age, upplim, BTSettl_str=True)
SPOTS = SPOTS_edr3
f = '85'
minim_age = 0.02
maxim_age = 0.120
upplim = 0.2
plot_analyzer = PlotAnalyzer(path_all)
plot_analyzer.plot_ages(SPOTS, f, minim_age, maxim_age, upplim, BTSettl_str=True)
for age in SPOTS_edr3['00'].keys(): if age >= 0.08 and age < 0.2: print('____________', age) print(SPOTS_edr3['00'][age][['Teff', 'A(Li)']])
fig, ax = plt.subplots(1, 1, figsize=(8, 6))
ax.set_ylabel(r'$A(Li)$ [dex]')
ax.set_xlabel(r'$T_{eff}$ [mag]')
ax.plot(BTSettl_Li_isochrones[0.6]['Teff'], BTSettl_Li_isochrones[0.6]['A(Li)'], linewidth=1, label='600 Myr', linestyle='--', color='r')
ax.plot(BTSettl_Li_isochrones[0.7]['Teff'], BTSettl_Li_isochrones[0.7]['A(Li)'], linewidth=1, label='700 Myr', linestyle='--', color='b')
ax.plot(BTSettl_Li_isochrones[0.8]['Teff'], BTSettl_Li_isochrones[0.8]['A(Li)'], linewidth=1, label='800 Myr', linestyle='--', color='k')
ax.errorbar(data_obs_Pleiades['Teff'], data_obs_Pleiades['ALi'], yerr=data_obs_Pleiades['e_ALi'], fmt='.', zorder=0, color='r', elinewidth=1, capsize=2)
ax.invert_xaxis()
ax.legend(fontsize=12)
<matplotlib.legend.Legend at 0x7f5908f11ad0>
BTSettl ages for SPOTS¶
plt.rcParams.update({'font.size': 11, 'axes.linewidth': 1, 'axes.edgecolor': 'k'})
plt.rcParams['font.family'] = 'serif'
ages_BTSettl = list(BTSettl_Li_isochrones.keys())
ages_SPOTS = list(SPOTS_edr3['00'].keys())
SPOTS_edr3_00 = {}
BTSettl_Li_isochrones_MS = {}
for age in BTSettl_Li_isochrones.keys():
if age <= 4.0 and age >= 0.004:
closest_age = ages_SPOTS[np.abs(ages_SPOTS - age).argmin()]
SPOTS_edr3_00[closest_age] = SPOTS_edr3['00'][closest_age]
BTSettl_Li_isochrones_MS[age] = BTSettl_Li_isochrones[age]
SPOTS_edr3_full = {}
for f in SPOTS_edr3.keys():
SPOTS_edr3_full[f] = {}
ages_SPOTS = list(SPOTS_edr3[f].keys())
for age in BTSettl_Li_isochrones.keys():
if age <= 4.0 and age >= 0.004:
closest_age = ages_SPOTS[np.abs(ages_SPOTS - age).argmin()]
SPOTS_edr3_full[f][closest_age] = SPOTS_edr3[f][closest_age]
BTSettl_Li_isochrones_MS[age] = BTSettl_Li_isochrones[age]
SPOTS_edr3_00.keys()
dict_keys([0.004, 0.005, 0.006, 0.007, 0.008, 0.009, 0.01, 0.02, 0.028, 0.04, 0.05, 0.063, 0.071, 0.079, 0.089, 0.1, 0.126, 0.158, 0.2, 0.316, 0.398, 0.501, 0.631, 0.708, 0.794, 0.891, 1.0, 1.995, 3.162, 3.981])
BTSettl_Li_isochrones_MS.keys()
dict_keys([0.004, 0.005, 0.006, 0.007, 0.008, 0.009, 0.01, 0.02, 0.03, 0.04, 0.05, 0.06, 0.07, 0.08, 0.09, 0.1, 0.12, 0.15, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0, 2.0, 3.0, 4.0])
from models_test import SPOTS_extended
BTSettl = BTSettl_Li_isochrones_MS
SPOTS = SPOTS_edr3_full
MS_color_file = 'MS_color.csv'
f = ['00', '17', '34', '51', '68', '85']
# Must be dics!!!! ^^^^
SPOTS_expanded, BTSettl_Li_isochrones_Teff_dic = SPOTS_extended(MS_color_file, BTSettl, SPOTS, f).SPOTS_extension()
SPOTS_expanded['00'][0.126]
| logAge | Mass | Fspot | Xspot | log(L/Lsun) | log(R/Rsun) | log(g) | log(Teff) | log(T_hot) | log(T_cool) | ... | H-K | G-H | G-K | G-V | A(Li) | Lsun | Teff | M_bol | BCv | RP-J | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 8.100000 | 1.300 | 0.0 | 0.8 | 0.412322 | 0.101930 | 4.347365 | 3.813743 | 3.813743 | 0.0000 | ... | 0.03369 | 0.94187 | 0.97556 | -0.15675 | 3.246089 | 2.584178 | 6512.426278 | 3.721184 | NaN | 0.36903 |
| 1 | 8.100000 | 1.250 | 0.0 | 0.8 | 0.330458 | 0.079603 | 4.374986 | 3.804440 | 3.804440 | 0.0000 | ... | 0.03575 | 1.00924 | 1.04499 | -0.16294 | 3.226090 | 2.140217 | 6374.414055 | 3.925846 | NaN | 0.39482 |
| 2 | 8.100000 | 1.200 | 0.0 | 0.8 | 0.244443 | 0.055355 | 4.405754 | 3.795061 | 3.795061 | 0.0000 | ... | 0.03804 | 1.07753 | 1.11557 | -0.16850 | 3.199054 | 1.755672 | 6238.221627 | 4.140882 | NaN | 0.42129 |
| 3 | 8.100000 | 1.150 | 0.0 | 0.8 | 0.154305 | 0.029990 | 4.437999 | 3.785208 | 3.785208 | 0.0000 | ... | 0.04084 | 1.14885 | 1.18969 | -0.17206 | 3.162471 | 1.426608 | 6098.292624 | 4.366229 | NaN | 0.44928 |
| 4 | 8.100000 | 1.100 | 0.0 | 0.8 | 0.060162 | 0.003835 | 4.471005 | 3.774750 | 3.774750 | 0.0000 | ... | 0.04458 | 1.22739 | 1.27197 | -0.17662 | 3.113608 | 1.148582 | 5953.197490 | 4.601585 | NaN | 0.48050 |
| 5 | 8.100000 | 1.050 | 0.0 | 0.8 | -0.037916 | -0.022712 | 4.503896 | 3.763504 | 3.763504 | 0.0000 | ... | 0.04858 | 1.31496 | 1.36354 | -0.20177 | 3.047699 | 0.916397 | 5801.018360 | 4.846781 | NaN | 0.51578 |
| 6 | 8.100000 | 1.000 | 0.0 | 0.8 | -0.140140 | -0.048660 | 4.534602 | 3.750922 | 3.750922 | 0.0000 | ... | 0.05284 | 1.40895 | 1.46179 | -0.22926 | 2.958403 | 0.724203 | 5635.369148 | 5.102339 | NaN | 0.55408 |
| 7 | 8.100000 | 0.950 | 0.0 | 0.8 | -0.246858 | -0.073722 | 4.562450 | 3.736774 | 3.736774 | 0.0000 | ... | 0.05927 | 1.52179 | 1.58106 | -0.24622 | 2.831645 | 0.566424 | 5454.735418 | 5.369136 | NaN | 0.60046 |
| 8 | 8.100000 | 0.900 | 0.0 | 0.8 | -0.358181 | -0.098047 | 4.587620 | 3.721106 | 3.721106 | 0.0000 | ... | 0.06548 | 1.65187 | 1.71735 | -0.26414 | 2.641553 | 0.438348 | 5261.455359 | 5.647442 | NaN | 0.65401 |
| 9 | 8.100000 | 0.850 | 0.0 | 0.8 | -0.474711 | -0.121780 | 4.610261 | 3.703839 | 3.703839 | 0.0000 | ... | 0.07325 | 1.80349 | 1.87674 | -0.30101 | 2.349644 | 0.335189 | 5056.377615 | 5.938767 | NaN | 0.71601 |
| 10 | 8.100000 | 0.800 | 0.0 | 0.8 | -0.597804 | -0.145327 | 4.631026 | 3.684840 | 3.684840 | 0.0000 | ... | 0.08276 | 1.97991 | 2.06267 | -0.34286 | 1.881039 | 0.252462 | 4839.938464 | 6.246499 | NaN | 0.78762 |
| 11 | 8.100000 | 0.750 | 0.0 | 0.8 | -0.728511 | -0.169368 | 4.651080 | 3.664184 | 3.664184 | 0.0000 | ... | 0.09376 | 2.19008 | 2.28384 | -0.40592 | 1.075974 | 0.186848 | 4615.127249 | 6.573268 | NaN | 0.87372 |
| 12 | 8.100000 | 0.700 | 0.0 | 0.8 | -0.864977 | -0.195221 | 4.672823 | 3.642994 | 3.642994 | 0.0000 | ... | 0.11210 | 2.40327 | 2.51537 | -0.46759 | -0.421246 | 0.136466 | 4395.352697 | 6.914432 | NaN | 0.96956 |
| 13 | 8.100000 | 0.650 | 0.0 | 0.8 | -1.012628 | -0.226149 | 4.702494 | 3.621545 | 3.621545 | 0.0000 | ... | 0.14166 | 2.57682 | 2.71848 | -0.55648 | -inf | 0.097134 | 4183.549674 | 7.283560 | NaN | 1.06201 |
| 14 | 8.100000 | 0.600 | 0.0 | 0.8 | -1.152988 | -0.260413 | 4.736260 | 3.603587 | 3.603587 | 0.0000 | ... | 0.16852 | 2.69514 | 2.86366 | -0.61492 | -inf | 0.070309 | 4014.089117 | 7.634459 | NaN | 1.13691 |
| 15 | 8.100000 | 0.550 | 0.0 | 0.8 | -1.268473 | -0.291167 | 4.759979 | 3.590093 | 3.590093 | 0.0000 | ... | 0.18849 | 2.78150 | 2.96999 | -0.68095 | -inf | 0.053892 | 3891.281506 | 7.923174 | NaN | 1.19831 |
| 16 | 8.100000 | 0.500 | 0.0 | 0.8 | -1.414063 | -0.329963 | 4.796178 | 3.573093 | 3.573093 | 0.0000 | ... | 0.20717 | 2.90841 | 3.11558 | -0.81893 | -inf | 0.038542 | 3741.906660 | 8.287148 | NaN | 1.29024 |
| 17 | 8.100000 | 0.450 | 0.0 | 0.8 | -1.560148 | -0.373196 | 4.836887 | 3.558189 | 3.558189 | 0.0000 | ... | 0.21722 | 3.03013 | 3.24735 | -0.92722 | -inf | 0.027533 | 3615.667953 | 8.652360 | NaN | 1.37750 |
| 18 | 8.100000 | 0.400 | 0.0 | 0.8 | -1.690418 | -0.414652 | 4.868646 | 3.546349 | 3.546349 | 0.0000 | ... | 0.22274 | 3.12717 | 3.34991 | -1.00765 | -inf | 0.020398 | 3518.427448 | 8.978036 | NaN | 1.44727 |
| 19 | 8.100000 | 0.350 | 0.0 | 0.8 | -1.810935 | -0.454479 | 4.890308 | 3.536133 | 3.536133 | 0.0000 | ... | 0.22728 | 3.21378 | 3.44106 | -1.10319 | -inf | 0.015455 | 3436.631053 | 9.279328 | NaN | 1.50979 |
| 20 | 8.100000 | 0.300 | 0.0 | 0.8 | -1.925703 | -0.496960 | 4.908324 | 3.528682 | 3.528682 | 0.0000 | ... | 0.23084 | 3.27957 | 3.51041 | -1.18325 | -inf | 0.011866 | 3378.171160 | 9.566247 | NaN | 1.55754 |
| 21 | 8.100000 | 0.250 | 0.0 | 0.8 | -2.050513 | -0.543744 | 4.922710 | 3.520871 | 3.520871 | 0.0000 | ... | 0.23494 | 3.35311 | 3.58805 | -1.28022 | -inf | 0.008902 | 3317.959343 | 9.878273 | NaN | 1.61108 |
| 22 | 8.100000 | 0.200 | 0.0 | 0.8 | -2.201137 | -0.599432 | 4.937177 | 3.511059 | 3.511059 | 0.0000 | ... | 0.24098 | 3.45150 | 3.69248 | -1.43388 | -inf | 0.006293 | 3243.838470 | 10.254833 | NaN | 1.68260 |
| 23 | 8.100000 | 0.150 | 0.0 | 0.8 | -2.395952 | -0.669096 | 4.951565 | 3.497187 | 3.497187 | 0.0000 | ... | 0.25040 | 3.60048 | 3.85088 | -1.73221 | -inf | 0.004018 | 3141.861887 | 10.741871 | NaN | 1.79121 |
| 24 | 8.100000 | 0.100 | 0.0 | 0.8 | -2.689492 | -0.761060 | 4.959401 | 3.469784 | 3.469784 | 0.0000 | ... | 0.26728 | 3.95060 | 4.21788 | -2.47892 | -inf | 0.002044 | 2949.742462 | 11.475720 | NaN | 2.05097 |
| 25 | 8.100371 | 0.095 | 0.0 | 0.8 | -2.790000 | -0.806875 | 5.028756 | 3.467000 | 3.467000 | 2.7736 | ... | 0.32900 | 4.21800 | 4.55000 | -1.95000 | -0.700000 | 0.001622 | 2930.893245 | 6.772189 | -3.58 | 2.25000 |
| 26 | 8.100371 | 0.090 | 0.0 | 0.8 | -2.980000 | -0.863279 | 5.118083 | 3.449000 | 3.449000 | 2.7592 | ... | 0.35200 | 4.37500 | 4.73000 | -2.37000 | 2.345323 | 0.001047 | 2811.900830 | 6.913199 | -4.13 | 2.34000 |
| 27 | 8.100371 | 0.085 | 0.0 | 0.8 | -3.100000 | -0.899629 | 5.165960 | 3.438000 | 3.438000 | 2.7504 | ... | 0.36000 | 4.53900 | 4.90000 | -2.70000 | 2.687390 | 0.000794 | 2741.574172 | 7.004074 | -4.62 | 2.45000 |
| 29 | 8.100371 | 0.080 | 0.0 | 0.8 | -3.240000 | -0.935542 | 5.211456 | 3.420000 | 3.420000 | 2.7360 | ... | 0.42200 | 4.78000 | 5.20000 | -3.15000 | 3.254725 | 0.000575 | 2630.267992 | 7.093855 | -5.32 | 2.58000 |
| 31 | 8.100371 | 0.077 | 0.0 | 0.8 | -3.470000 | -0.982967 | 5.289706 | 3.384000 | 3.384000 | 2.7072 | ... | 0.46500 | 5.34000 | 5.81000 | -3.09000 | 3.298695 | 0.000339 | 2421.029047 | 7.212417 | -5.78 | 3.06000 |
| 34 | 8.100371 | 0.074 | 0.0 | 0.8 | -3.600000 | -0.991400 | 5.289313 | 3.356000 | 3.356000 | 2.6848 | ... | 0.51000 | 5.55000 | 6.00000 | -3.45000 | 3.300000 | 0.000251 | 2269.864852 | 7.233500 | -6.25 | 3.11000 |
| 38 | 8.100371 | 0.068 | 0.0 | 0.8 | -4.010000 | -1.026872 | 5.323535 | 3.272000 | 3.272000 | 2.6176 | ... | 0.63000 | 10.21000 | 10.80000 | 0.00000 | 3.300000 | 0.000098 | 1870.682140 | 7.322180 | -7.53 | 7.47000 |
32 rows × 63 columns
fig, ax = plt.subplots(1, 1, figsize=(8, 6))
ax.set_ylabel('$A(Li)$ [dex]')
ax.set_xlabel('G-RP [mag]')
ax.plot(SPOTS_edr3['00'][0.126]['G_abs'] - SPOTS_edr3['00'][0.126]['RP_abs'], SPOTS_edr3['00'][0.126]['A(Li)'], linewidth=1, label='SPOTS f000')
ax.plot(SPOTS_edr3['17'][0.126]['G_abs'] - SPOTS_edr3['17'][0.126]['RP_abs'], SPOTS_edr3['00'][0.126]['A(Li)'], linewidth=1, label='SPOTS f017')
ax.plot(SPOTS_edr3['34'][0.126]['G_abs'] - SPOTS_edr3['34'][0.126]['RP_abs'], SPOTS_edr3['00'][0.126]['A(Li)'], linewidth=1, label='SPOTS f034')
ax.plot(SPOTS_edr3['51'][0.126]['G_abs'] - SPOTS_edr3['51'][0.126]['RP_abs'], SPOTS_edr3['00'][0.126]['A(Li)'], linewidth=1, label='SPOTS f051')
#ax.scatter(SPOTS_edr3_expanded['00'][0.126]['G-RP'], SPOTS_edr3_expanded['00'][0.126]['A(Li)'], s=10, zorder=4, label='SPOTS expanded')
ax.plot(SPOTS_expanded['00'][0.126]['G-RP'], SPOTS_expanded['00'][0.126]['A(Li)'], lw=1, ls=':', color='b', zorder=4, label='SPOTS expanded')
ax.plot(BTSettl_Li_isochrones[0.120]['G_abs'] - BTSettl_Li_isochrones[0.120]['RP_abs'], BTSettl_Li_isochrones[0.120]['A(Li)'], linewidth=1, label='BT-Settl', linestyle='--', color='k')
ax.errorbar(data_obs_Pleiades['G_abs'] - data_obs_Pleiades['RP_abs'], data_obs_Pleiades['ALi'], yerr=data_obs_Pleiades['e_ALi'], fmt='.', zorder=0, color='r', elinewidth=1, capsize=2)
fig.legend(fontsize=12, loc='lower center', ncol=3, bbox_to_anchor=(0.525, -0.1))
<matplotlib.legend.Legend at 0x7fd7231d9210>
fig, ax = plt.subplots(1, 1, figsize=(8, 6))
ax.set_ylabel('$A(Li)$ [dex]')
ax.set_xlabel('G-J [mag]')
ax.plot(SPOTS_edr3['00'][0.126]['G_abs'] - SPOTS_edr3['00'][0.126]['J_mag'], SPOTS_edr3['00'][0.126]['A(Li)'], linewidth=1, label='SPOTS f000')
ax.plot(SPOTS_edr3['17'][0.126]['G_abs'] - SPOTS_edr3['17'][0.126]['J_mag'], SPOTS_edr3['00'][0.126]['A(Li)'], linewidth=1, label='SPOTS f017')
ax.plot(SPOTS_edr3['34'][0.126]['G_abs'] - SPOTS_edr3['34'][0.126]['J_mag'], SPOTS_edr3['00'][0.126]['A(Li)'], linewidth=1, label='SPOTS f034')
ax.plot(SPOTS_edr3['51'][0.126]['G_abs'] - SPOTS_edr3['51'][0.126]['J_mag'], SPOTS_edr3['00'][0.126]['A(Li)'], linewidth=1, label='SPOTS f051')
#ax.scatter(SPOTS_edr3_expanded['00'][0.126]['G-RP'], SPOTS_edr3_expanded['00'][0.126]['A(Li)'], s=10, zorder=4, label='SPOTS expanded')
ax.plot(SPOTS_expanded['00'][0.126]['G-J'], SPOTS_expanded['00'][0.126]['A(Li)'], lw=1, ls=':', color='b', zorder=4, label='SPOTS expanded')
ax.plot(BTSettl_Li_isochrones[0.120]['G_abs'] - BTSettl_Li_isochrones[0.120]['J_abs'], BTSettl_Li_isochrones[0.120]['A(Li)'], linewidth=1, label='BT-Settl', linestyle='--', color='k')
ax.errorbar(data_obs_Pleiades['G_abs'] - data_obs_Pleiades['J_abs'], data_obs_Pleiades['ALi'], yerr=data_obs_Pleiades['e_ALi'], fmt='.', zorder=0, color='r', elinewidth=1, capsize=2)
ax.set_xlim(0, 6)
fig.legend(fontsize=12, loc='lower center', ncol=3, bbox_to_anchor=(0.525, -0.1))
<matplotlib.legend.Legend at 0x7fd74409f510>
SPOTS_extended(MS_color_file, BTSettl, SPOTS, f).plot_CMD_ALi(SPOTS_expanded, BTSettl_Li_isochrones_Teff_dic, BTSettl_Li_isochrones, data_obs_Pleiades)
ages_SPOTS = np.array([float(age) for age in SPOTS_edr3['00'].keys()])
def plot_SPOTS_data(age_array, f_array, band1, band2, bandobs):
"""
Plot SPOTS data with customizable age, f, and band combinations.
Parameters:
age_array (array-like): array of age values in Gyr.
f_array (array-like): array of f values.
band1 (str): first band for plotting.
band2 (str): second band for plotting.
bandobs (str): band for observational data.
"""
plt.rcParams.update({'font.size': 12}) # Set the font size
fig, ax = plt.subplots(1, 1, figsize=(8, 6))
ax.set_ylabel(f'{band1} [mag]')
ax.set_xlabel(f'{band1}-{band2} [mag]')
# Color map creation
cmap = plt.get_cmap('tab10')
colors = [cmap(i) for i in np.linspace(0, 1, len(age_array))]
# Line style array
ls_array = ['-', '--', '-.', ':']
for i, age in enumerate(age_array):
age_Myr = age * 1000
for j, f in enumerate(f_array):
closest_age = ages_SPOTS[np.abs(ages_SPOTS - age).argmin()]
if closest_age in SPOTS_edr3[f]:
ax.plot(
SPOTS_edr3[f][closest_age][f'{band1}_abs'] - SPOTS_edr3[f][closest_age][f'{band2}_abs'],
SPOTS_edr3[f][closest_age][f'{band1}_abs'],
label=f'SPOTS-YBC f0{f}; {age_Myr} Myr',
color=colors[i],
lw=1,
ls=ls_array[j % len(ls_array)]
)
else:
print(f"Closest age {closest_age} not found in SPOTS_edr3[f][{f}]")
ax.scatter(data_obs_Pleiades[f'{band1}_abs'] - data_obs_Pleiades[f'{band2}_abs'], data_obs_Pleiades[f'{bandobs}_abs'], s=10, zorder=0, color='r', alpha=0.125)
ax.scatter(BTSettl_Li_isochrones_Teff[f'{band1}_abs'] - BTSettl_Li_isochrones_Teff[f'{band2}_abs'], BTSettl_Li_isochrones_Teff[f'{bandobs}_abs'], s=10, label='Low-mass stars BT-Settl')
ax.legend(fontsize=10, loc='upper center', bbox_to_anchor=(1.25, 0.9))
ax.invert_yaxis()
plt.show()
age_array = [0.001, 0.02, 0.120, 0.6, 4]
f_array = ['00', '51', '85']
plot_SPOTS_data(age_array, f_array, 'G', 'RP', 'G')
age_array = [0.001, 0.02, 0.120, 0.6, 4] f_array = ['00']
plot_SPOTS_data(age_array, f_array, 'G', 'RP', 'G')
plot_SPOTS_data(age_array, f_array, 'G', 'J', 'G')
MS_color = pd.read_csv('MS_color.csv')
for c in MS_color.columns:
if c != '#SpT':
MS_color[c] = pd.to_numeric(MS_color[c], errors='coerce')
MS_color = MS_color[10**MS_color['logT'] < 6000]
MS_color = MS_color[10**MS_color['logT'] > 1600]
def find_nearest_index(array, value):
array = np.asarray(array)
idx = (np.abs(array - value)).argmin()
return idx
def calculate_log_g(mass, radius):
"""
Calculate the surface gravity log(g) of a star.
Parameters:
- mass: Mass of the star in solar masses.
- radius: Radius of the star in solar radii.
Returns:
- log_g: The surface gravity log(g) in cgs units.
"""
G = 6.67430e-11 # gravitational constant in m^3 kg^-1 s^-2
M_sun = 1.98847e30 # mass of the Sun in kg
R_sun = 6.9634e8 # radius of the Sun in meters
# Convert mass and radius to SI units
mass_kg = mass * M_sun
radius_m = radius * R_sun
# Calculate g
g = G * mass_kg / (radius_m ** 2)
# Convert g to cgs units (cm/s^2)
g_cgs = g * 100 # 1 m/s^2 = 100 cm/s^2
# Calculate log(g)
log_g = np.log10(g_cgs)
return log_g
def SPOTS_extension(MS_color, BTSettl, SPOTS, f):
"""
Extend SPOTS data using provided inputs.
Parameters:
MS_color (DataFrame): DataFrame for Phot. correction (Pecaut & Mamajek 2013)
BTSettl (dict): Dictionary with BTSettl data.
SPOTS (dict): Dictionary with SPOTS data.
f (str): Factor value to use.
Returns:
SPOTS_expanded
"""
M_bol_sun = 4.755
Teff_sun = 5772
Teff_max_array_SPOTS = []
Teff_min_array_SPOTS = []
age_array_SPOTS = []
Teff_max_array_SPOTS_Li = []
Teff_min_array_SPOTS_Li = []
age_array_SPOTS_Li = []
inf_index_SPOTS_array = []
j = 0
for age in SPOTS.keys():
age_array_SPOTS.append(age)
Teff_max = max(SPOTS[age]['Teff'])
Teff_min = min(SPOTS[age]['Teff'])
Teff_max_array_SPOTS.append(Teff_max)
Teff_min_array_SPOTS.append(Teff_min)
j += 1
if age <= 0.7:
Teff_series = np.array(SPOTS[age]['Teff'])
A_Li_series = np.array(SPOTS[age]['A(Li)'])
Teff_min_all = min(Teff_series)
Teff_max_all = max(Teff_series)
Teff_max = None
for i in range(len(Teff_series)):
if A_Li_series[i] == -np.inf:
Teff_min = Teff_series[i-1]
if i == 0:
Teff_min = Teff_min_all
else:
pass
inf_index_SPOTS_array.append(j - 1)
break
for i in range(len(Teff_series)-1, -1, -1):
if A_Li_series[i] == -np.inf:
Teff_max = Teff_series[i]
break
if Teff_max is not None:
age_array_SPOTS_Li.append(age)
Teff_max_array_SPOTS_Li.append(Teff_max)
Teff_min_array_SPOTS_Li.append(Teff_min)
Teff_max_array_BTSettl = []
Teff_min_array_BTSettl = []
age_array_BTSettl = []
Teff_max_array_BTSettl_Li = []
Teff_min_array_BTSettl_Li = []
age_array_BTSettl_Li = []
for age in BTSettl.keys():
age_array_BTSettl.append(age)
Teff_max = max(BTSettl[age]['Teff'])
Teff_min = min(BTSettl[age]['Teff'])
Teff_max_array_BTSettl.append(Teff_max)
Teff_min_array_BTSettl.append(Teff_min)
if age <= 0.7:
Teff_series = np.array(BTSettl[age]['Teff'])
A_Li_series = np.array(BTSettl[age]['A(Li)'])
Teff_min = None
Teff_max = None
for i in range(len(Teff_series)):
if A_Li_series[i] == -np.inf:
inf_index_BTSettl = i-1
Teff_min = Teff_series[i-1]
break
for i in range(len(Teff_series)-1, -1, -1):
if A_Li_series[i] == -np.inf:
Teff_max = Teff_series[i+1]
break
if Teff_max is not None:
age_array_BTSettl_Li.append(age)
Teff_max_array_BTSettl_Li.append(Teff_max)
Teff_min_array_BTSettl_Li.append(Teff_min)
MS_color['G-J'] = MS_color['G-V'] + MS_color['Mv'] - MS_color['M_J']
MS_color['G-Ks'] = MS_color['G-V'] + MS_color['Mv'] - MS_color['M_Ks']
MS_color['Teff'] = 10**MS_color['logT']
MS_color = MS_color.reset_index()
MS_color_filtered = MS_color[(MS_color['Teff'] >= 1750) & (MS_color['Teff'] <= 3000)].copy()
MS_color_filtered.loc[:, 'logR'] = np.log10(MS_color_filtered['R_Rsun'])
n = MS_color_filtered.shape[0]
SPOTS_expanded = {}
age_index = 0
BTSettl_Li_isochrones_Teff_dic = {}
count = 0
for Teff_SPOTS_min in Teff_min_array_SPOTS:
age_BTSettl = list(BTSettl.keys())[age_index]
age_SPOTS = list(SPOTS.keys())[age_index]
if inf_index_SPOTS_array[0] >= age_index:
age_index += 1
count += 1
BTSettl_Li_isochrones_Teff = BTSettl[age_BTSettl][BTSettl[age_BTSettl]['Teff'] < Teff_SPOTS_min]
BTSettl_Li_isochrones_Teff = BTSettl_Li_isochrones_Teff[BTSettl_Li_isochrones_Teff['Teff'] > min(BTSettl_Li_isochrones_Teff['Teff'])]
BTSettl_Li_isochrones_Teff = BTSettl_Li_isochrones_Teff.sort_values(by='Teff', ascending=False)
BTSettl_Li_isochrones_Teff['log(Teff)'] = np.log10(BTSettl_Li_isochrones_Teff['Teff'])
BTSettl_Li_isochrones_Teff_dic[age_BTSettl] = BTSettl_Li_isochrones_Teff.reset_index()
SPOTS[age_SPOTS]['log(Teff)'] = np.log10(SPOTS[age_SPOTS]['Teff'])
SPOTS[age_SPOTS]['M_bol'] = - 10 * np.log10(SPOTS[age_SPOTS]['Teff']/Teff_sun) - 5 * SPOTS[age_SPOTS]['log(R/Rsun)'] + M_bol_sun
SPOTS[age_SPOTS] = SPOTS[age_SPOTS].sort_values(by='log(Teff)', ascending=False)
SPOTS_expanded[age_SPOTS] = SPOTS[age_SPOTS]
SPOTS_expanded[age_SPOTS]['Lsun'] = 10**SPOTS_expanded[age_SPOTS]['log(L/Lsun)']
SPOTS_expanded[age_SPOTS]['M_bol'] = M_bol_sun - 2.5 * SPOTS_expanded[age_SPOTS]['log(R/Rsun)']
SPOTS_expanded[age_SPOTS]['G_abs'] = SPOTS_expanded[age_SPOTS]['G-V'] + SPOTS_expanded[age_SPOTS]['V_mag']
SPOTS_expanded[age_SPOTS]['J_abs'] = - SPOTS_expanded[age_SPOTS]['G-J'] + SPOTS_expanded[age_SPOTS]['G_abs']
SPOTS_expanded[age_SPOTS]['RP_abs'] = - SPOTS_expanded[age_SPOTS]['G-RP'] + SPOTS_expanded[age_SPOTS]['G_abs']
SPOTS_expanded[age_SPOTS]['BP_abs'] = SPOTS_expanded[age_SPOTS]['BP-RP'] + SPOTS_expanded[age_SPOTS]['RP_abs']
SPOTS_expanded[age_SPOTS]['H_abs'] = - SPOTS_expanded[age_SPOTS]['J-H'] + SPOTS_expanded[age_SPOTS]['J_abs']
SPOTS_expanded[age_SPOTS]['K_abs'] = - SPOTS_expanded[age_SPOTS]['G-K'] + SPOTS_expanded[age_SPOTS]['G_abs']
SPOTS_expanded[age_SPOTS]['RP-J'] = SPOTS_expanded[age_SPOTS]['RP_abs'] - SPOTS_expanded[age_SPOTS]['J_abs']
SPOTS_expanded[age_SPOTS]['G-H'] = SPOTS_expanded[age_SPOTS]['G_abs'] - SPOTS_expanded[age_SPOTS]['H_abs']
SPOTS_expanded[age_SPOTS] = SPOTS_expanded[age_SPOTS][~pd.isna(SPOTS_expanded[age_SPOTS]['A(Li)'])]
for Teff_BTSettl_min_Li, Teff_SPOTS_min in zip(Teff_min_array_BTSettl_Li, Teff_min_array_SPOTS):
age_BTSettl = list(BTSettl.keys())[age_index]
age_SPOTS = list(SPOTS.keys())[age_index]
if Teff_BTSettl_min_Li <= Teff_SPOTS_min:
age_index += 1
count = count + 1
BTSettl_Li_isochrones_Teff = BTSettl[age_BTSettl][BTSettl[age_BTSettl]['Teff'] < Teff_SPOTS_min]
BTSettl_Li_isochrones_Teff = BTSettl_Li_isochrones_Teff[BTSettl_Li_isochrones_Teff['Teff'] > min(BTSettl_Li_isochrones_Teff['Teff'])]
BTSettl_Li_isochrones_Teff = BTSettl_Li_isochrones_Teff.sort_values(by='Teff', ascending=False)
BTSettl_Li_isochrones_Teff['log(Teff)'] = np.log10(BTSettl_Li_isochrones_Teff['Teff'])
BTSettl_Li_isochrones_Teff = BTSettl_Li_isochrones_Teff.reset_index()
BTSettl_Li_isochrones_Teff_dic[age_BTSettl] = BTSettl_Li_isochrones_Teff
MS_color_rows = {}
MS_color_rows[age_SPOTS] = pd.DataFrame(np.nan, index=range(n), columns=SPOTS[age_SPOTS].columns)
SPOTS[age_SPOTS]['G-V'] = SPOTS[age_SPOTS]['G_abs'] - SPOTS[age_SPOTS]['V_mag']
SPOTS[age_SPOTS]['log(Teff)'] = np.log10(SPOTS[age_SPOTS]['Teff'])
SPOTS[age_SPOTS]['M_bol'] = - 10 * np.log10(SPOTS[age_SPOTS]['Teff']/Teff_sun) - 5 * SPOTS[age_SPOTS]['log(R/Rsun)'] + M_bol_sun
SPOTS[age_SPOTS] = SPOTS[age_SPOTS].sort_values(by='log(Teff)', ascending=False)
SPOTS_expanded[age_SPOTS] = pd.concat([SPOTS[age_SPOTS], MS_color_rows[age_SPOTS]], ignore_index=True)
columns_map = {
'Mass': 'Msun',
'Teff': 'Teff',
'log(Teff)': 'logT',
'log(L/Lsun)': 'logL',
'log(R/Rsun)': 'logR',
'G-RP': 'G-Rp',
'G-V': 'G-V',
'G-J': 'G-J',
'BP-RP': 'Bp-Rp',
'J-H': 'J-H',
'H-K': 'H-Ks',
'G-K': 'G-Ks',
'V_mag': 'Mv'
}
SPOTS_expanded[age_SPOTS]['BCv'] = np.nan
start_index = len(SPOTS[age_SPOTS])
for bc_col, spots_col in columns_map.items():
SPOTS_expanded[age_SPOTS].loc[start_index:, bc_col] = MS_color_filtered[spots_col].values
SPOTS_expanded[age_SPOTS]['Lsun'] = 10**SPOTS_expanded[age_SPOTS]['log(L/Lsun)']
SPOTS_expanded[age_SPOTS].loc[start_index:, 'BCv'] = MS_color_filtered['BCv'].values
for i in range(SPOTS[age_SPOTS]['logAge'].index[0]+1, len(SPOTS_expanded[age_SPOTS]['logAge'])):
SPOTS_expanded[age_SPOTS].loc[i, 'logAge'] = np.log10(age_SPOTS*1e9)
SPOTS_expanded[age_SPOTS].loc[i, 'Fspot'] = 0.0
SPOTS_expanded[age_SPOTS].loc[i, 'Age_Gyr'] = SPOTS_expanded[age_SPOTS]['Age_Gyr'][0]
SPOTS_expanded[age_SPOTS].loc[i, 'Xspot'] = 0.8
SPOTS_expanded[age_SPOTS].loc[i, 'log(T_hot)'] = SPOTS_expanded[age_SPOTS]['log(Teff)'][i]
SPOTS_expanded[age_SPOTS].loc[i, 'log(T_cool)'] = 0.0
SPOTS_expanded[age_SPOTS].loc[i, 'M_bol'] = M_bol_sun - 2.5 * SPOTS_expanded[age_SPOTS].loc[i, 'log(R/Rsun)']
SPOTS_expanded[age_SPOTS].loc[i, 'G_abs'] = SPOTS_expanded[age_SPOTS].loc[i, 'G-V'] + SPOTS_expanded[age_SPOTS].loc[i, 'V_mag']
SPOTS_expanded[age_SPOTS].loc[i, 'J_abs'] = - SPOTS_expanded[age_SPOTS].loc[i, 'G-J'] + SPOTS_expanded[age_SPOTS].loc[i, 'G_abs']
SPOTS_expanded[age_SPOTS].loc[i, 'RP_abs'] = - SPOTS_expanded[age_SPOTS].loc[i, 'G-RP'] + SPOTS_expanded[age_SPOTS].loc[i, 'G_abs']
SPOTS_expanded[age_SPOTS].loc[i, 'BP_abs'] = SPOTS_expanded[age_SPOTS].loc[i, 'BP-RP'] + SPOTS_expanded[age_SPOTS].loc[i, 'RP_abs']
SPOTS_expanded[age_SPOTS].loc[i, 'H_abs'] = - SPOTS_expanded[age_SPOTS].loc[i, 'J-H'] + SPOTS_expanded[age_SPOTS].loc[i, 'J_abs']
SPOTS_expanded[age_SPOTS].loc[i, 'K_abs'] = - SPOTS_expanded[age_SPOTS].loc[i, 'G-K'] + SPOTS_expanded[age_SPOTS].loc[i, 'G_abs']
min_Teff_max = SPOTS[age_SPOTS]['Teff'].min()
min_Teff_min = 1600
for i, row in BTSettl_Li_isochrones_Teff.iterrows():
teff_value = row['Teff']
if i == 1 and BTSettl_Li_isochrones_Teff.at[0, 'A(Li)'] == -np.inf and row['A(Li)'] == -np.inf:
a_li_value = BTSettl_Li_isochrones_Teff.at[i + 1, 'A(Li)'] if i + 1 < len(BTSettl_Li_isochrones_Teff) else 0
else:
a_li_value = row['A(Li)']
nearest_index = find_nearest_index(SPOTS_expanded[age_SPOTS]['Teff'], teff_value)
SPOTS_expanded[age_SPOTS].at[nearest_index, 'A(Li)'] = a_li_value
SPOTS_expanded[age_SPOTS]['RP-J'] = SPOTS_expanded[age_SPOTS]['RP_abs'] - SPOTS_expanded[age_SPOTS]['J_abs']
SPOTS_expanded[age_SPOTS]['G-H'] = SPOTS_expanded[age_SPOTS]['G_abs'] - SPOTS_expanded[age_SPOTS]['H_abs']
SPOTS_expanded[age_SPOTS] = SPOTS_expanded[age_SPOTS][~pd.isna(SPOTS_expanded[age_SPOTS]['A(Li)'])]
else:
count = count + 1
age_index += 1
BTSettl_Li_isochrones_Teff = BTSettl[age_BTSettl][BTSettl[age_BTSettl]['Teff'] < Teff_SPOTS_min]
BTSettl_Li_isochrones_Teff = BTSettl_Li_isochrones_Teff[BTSettl_Li_isochrones_Teff['Teff'] > min(BTSettl_Li_isochrones_Teff['Teff'])]
BTSettl_Li_isochrones_Teff = BTSettl_Li_isochrones_Teff.sort_values(by='Teff', ascending=False)
BTSettl_Li_isochrones_Teff['log(Teff)'] = np.log10(BTSettl_Li_isochrones_Teff['Teff'])
BTSettl_Li_isochrones_Teff_dic[age_BTSettl] = BTSettl_Li_isochrones_Teff.reset_index()
MS_color_rows = {}
MS_color_rows[age_SPOTS] = pd.DataFrame(np.nan, index=range(n), columns=SPOTS[age_SPOTS].columns)
SPOTS[age_SPOTS]['G-V'] = SPOTS[age_SPOTS]['G_abs'] - SPOTS[age_SPOTS]['V_mag']
SPOTS[age_SPOTS]['log(Teff)'] = np.log10(SPOTS[age_SPOTS]['Teff'])
SPOTS[age_SPOTS]['M_bol'] = - 10 * np.log10(SPOTS[age_SPOTS]['Teff']/Teff_sun) - 5 * SPOTS[age_SPOTS]['log(R/Rsun)'] + M_bol_sun
SPOTS[age_SPOTS] = SPOTS[age_SPOTS].sort_values(by='log(Teff)', ascending=False)
SPOTS_expanded[age_SPOTS] = pd.concat([SPOTS[age_SPOTS], MS_color_rows[age_SPOTS]], ignore_index=True)
columns_map = {
'Mass': 'Msun',
'Teff': 'Teff',
'log(Teff)': 'logT',
'log(L/Lsun)': 'logL',
'log(R/Rsun)': 'logR',
'G-RP': 'G-Rp',
'G-V': 'G-V',
'G-J': 'G-J',
'BP-RP': 'Bp-Rp',
'J-H': 'J-H',
'H-K': 'H-Ks',
'G-K': 'G-Ks',
'V_mag': 'Mv'
}
SPOTS_expanded[age_SPOTS]['BCv'] = np.nan
start_index = len(SPOTS[age_SPOTS])
for bc_col, spots_col in columns_map.items():
SPOTS_expanded[age_SPOTS].loc[start_index:, bc_col] = MS_color_filtered[spots_col].values
SPOTS_expanded[age_SPOTS]['Lsun'] = 10**SPOTS_expanded[age_SPOTS]['log(L/Lsun)']
SPOTS_expanded[age_SPOTS].loc[start_index:, 'BCv'] = MS_color_filtered['BCv'].values
for i in range(SPOTS[age_SPOTS]['logAge'].index[0]+1, len(SPOTS_expanded[age_SPOTS]['logAge'])):
SPOTS_expanded[age_SPOTS].loc[i, 'logAge'] = np.log10(age_SPOTS*1e9)
SPOTS_expanded[age_SPOTS].loc[i, 'Fspot'] = 0.0
SPOTS_expanded[age_SPOTS].loc[i, 'Age_Gyr'] = SPOTS_expanded[age_SPOTS]['Age_Gyr'][0]
SPOTS_expanded[age_SPOTS].loc[i, 'Xspot'] = 0.8
SPOTS_expanded[age_SPOTS].loc[i, 'log(T_hot)'] = SPOTS_expanded[age_SPOTS]['log(Teff)'][i]
SPOTS_expanded[age_SPOTS].loc[i, 'log(T_cool)'] = 0.0
SPOTS_expanded[age_SPOTS].loc[i, 'M_bol'] = M_bol_sun - 2.5 * SPOTS_expanded[age_SPOTS].loc[i, 'log(R/Rsun)']
SPOTS_expanded[age_SPOTS].loc[i, 'G_abs'] = SPOTS_expanded[age_SPOTS].loc[i, 'G-V'] + SPOTS_expanded[age_SPOTS].loc[i, 'V_mag']
SPOTS_expanded[age_SPOTS].loc[i, 'J_abs'] = - SPOTS_expanded[age_SPOTS].loc[i, 'G-J'] + SPOTS_expanded[age_SPOTS].loc[i, 'G_abs']
SPOTS_expanded[age_SPOTS].loc[i, 'RP_abs'] = - SPOTS_expanded[age_SPOTS].loc[i, 'G-RP'] + SPOTS_expanded[age_SPOTS].loc[i, 'G_abs']
SPOTS_expanded[age_SPOTS].loc[i, 'BP_abs'] = SPOTS_expanded[age_SPOTS].loc[i, 'BP-RP'] + SPOTS_expanded[age_SPOTS].loc[i, 'RP_abs']
SPOTS_expanded[age_SPOTS].loc[i, 'H_abs'] = - SPOTS_expanded[age_SPOTS].loc[i, 'J-H'] + SPOTS_expanded[age_SPOTS].loc[i, 'J_abs']
SPOTS_expanded[age_SPOTS].loc[i, 'K_abs'] = - SPOTS_expanded[age_SPOTS].loc[i, 'G-K'] + SPOTS_expanded[age_SPOTS].loc[i, 'G_abs']
min_Teff_max = SPOTS[age_SPOTS]['Teff'].min()
min_Teff_min = 1600
for i, row in BTSettl_Li_isochrones_Teff.iterrows():
teff_value = row['Teff']
if i == 1 and BTSettl_Li_isochrones_Teff.at[0, 'A(Li)'] == -np.inf and row['A(Li)'] == -np.inf:
a_li_value = BTSettl_Li_isochrones_Teff.at[i + 1, 'A(Li)'] if i + 1 < len(BTSettl_Li_isochrones_Teff) else 0
else:
a_li_value = row['A(Li)']
nearest_index = find_nearest_index(SPOTS_expanded[age_SPOTS]['Teff'], teff_value)
SPOTS_expanded[age_SPOTS].at[nearest_index, 'A(Li)'] = a_li_value
SPOTS_expanded[age_SPOTS]['RP-J'] = SPOTS_expanded[age_SPOTS]['RP_abs'] - SPOTS_expanded[age_SPOTS]['J_abs']
SPOTS_expanded[age_SPOTS]['G-H'] = SPOTS_expanded[age_SPOTS]['G_abs'] - SPOTS_expanded[age_SPOTS]['H_abs']
SPOTS_expanded[age_SPOTS] = SPOTS_expanded[age_SPOTS][~pd.isna(SPOTS_expanded[age_SPOTS]['A(Li)'])]
SPOTS_expanded[age_SPOTS]['log(g)'] = calculate_log_g(SPOTS_expanded[age_SPOTS]['Mass'], 10**SPOTS_expanded[age_SPOTS]['log(R/Rsun)'])
return SPOTS_expanded, BTSettl_Li_isochrones_Teff_dic
BTSettl = BTSettl_Li_isochrones_MS
SPOTS = SPOTS_edr3_00
f = '00'
SPOTS_expanded_00, BTSettl_Li_isochrones_Teff_dic = SPOTS_extension(MS_color, BTSettl, SPOTS, f)
plt.rcParams.update({'font.size': 14, 'axes.linewidth': 1, 'axes.edgecolor': 'k'})
plt.rcParams['font.family'] = 'serif'
fig, axs = plt.subplots(2, 3, figsize=(20, 10), constrained_layout=True)
bands1 = ['RP_abs', 'G_abs', 'G_abs', 'G_abs', 'G_abs', 'J_abs', 'H_abs', 'J_abs', 'H_abs']
bands2 = ['J_abs', 'J_abs', 'H_abs', 'K_abs', 'RP_abs', 'H_abs', 'K_abs', 'H_abs', 'K_abs']
colors = ['RP-J', 'G-J', 'G-H', 'G-K', 'G-RP', 'J-H', 'H-K', 'J-H', 'H-K']
norm_BTSettl = plt.Normalize(vmin=np.log10(0.004*1e9), vmax=np.log10(0.6*1e9))
cmap_BTSettl = cm.ScalarMappable(norm=norm_BTSettl, cmap='Reds')
norm_SPOTS = plt.Normalize(vmin=np.log10(0.004*1e9), vmax=np.log10(0.6*1e9))
cmap_SPOTS = cm.ScalarMappable(norm=norm_SPOTS, cmap='Blues')
ages_BTSettl = np.array(list(BTSettl_Li_isochrones_Teff_dic.keys()))
ages_SPOTS = np.array(list(SPOTS_expanded_00.keys()))
for i, ax in enumerate(axs.flat):
band1 = bands1[i]
band2 = bands2[i]
color = colors[i]
ax.scatter(data_obs_Pleiades[band1]-data_obs_Pleiades[band2], data_obs_Pleiades['ALi'], s=10, zorder=6, color='orange', alpha=0.5)
ax.set_ylabel('$A(Li)$')
ax.set_xlabel(f'{color} [mag]')
max_array = []
for age_BTSettl in BTSettl_Li_isochrones_Teff_dic.keys():
if age_BTSettl < 0.6:
log_age_BTSettl = np.log10(age_BTSettl * 1e9)
color_BTSettl = cmap_BTSettl.to_rgba(log_age_BTSettl)
ax.plot(BTSettl_Li_isochrones_Teff_dic[age_BTSettl][band1] - BTSettl_Li_isochrones_Teff_dic[age_BTSettl][band2], BTSettl_Li_isochrones_Teff_dic[age_BTSettl]['A(Li)'], c=color_BTSettl, lw=1, alpha=1, label='Low mass BT-Settl' if i == 0 and age_BTSettl == 0.5 else "")
max_array.append(max(BTSettl_Li_isochrones_Teff_dic[age_BTSettl][band1] - BTSettl_Li_isochrones_Teff_dic[age_BTSettl][band2]))
for age_SPOTS in SPOTS_expanded_00.keys():
if age_SPOTS < 0.6:
log_age_SPOTS = np.log10(age_SPOTS * 1e9)
color_SPOTS = cmap_SPOTS.to_rgba(log_age_SPOTS)
ax.plot(SPOTS_expanded_00[age_SPOTS][color], SPOTS_expanded_00[age_SPOTS]['A(Li)'], lw=1, color=color_SPOTS, zorder=1, alpha=1, label='SPOTS extended' if i == 0 and age_SPOTS == 0.501 else "")
ax.plot(BTSettl_Li_isochrones[0.120][band1] - BTSettl_Li_isochrones[0.120][band2], BTSettl_Li_isochrones[0.120]['A(Li)'], lw=1, c='k', ls='--', alpha=1, label='Low mass BT-Settl 120 Myr' if i == 0 else "")
ax.plot(SPOTS_expanded_00[0.126][color], SPOTS_expanded_00[0.126]['A(Li)'], lw=1, color='k', ls='-', zorder=2, alpha=1, label='SPOTS extended 120 Myr' if i == 0 else "")
ax.set_xlim(0, max(max_array))
fig.colorbar(cmap_BTSettl, ax=axs[:, 2], orientation='vertical', label=r'$\log({\rm Age\ [yr]})$')
cbar = fig.colorbar(cmap_SPOTS, ax=axs[:, 2], orientation='vertical', label=r'$\log({\rm Age\ [yr]})$')
cbar.ax.set_yticklabels([])
cbar.ax.set_ylabel("")
fig.legend(loc='upper center', bbox_to_anchor=(0.5, 1.15))
plt.show()
plt.rcParams.update({'font.size': 11, 'axes.linewidth': 1, 'axes.edgecolor': 'k'})
plt.rcParams['font.family'] = 'serif'
plt.rcParams.update({'font.size': 14, 'axes.linewidth': 1, 'axes.edgecolor': 'k'})
plt.rcParams['font.family'] = 'serif'
fig, axs = plt.subplots(2, 3, figsize=(20, 10), constrained_layout=True)
bands1 = ['RP_abs', 'G_abs', 'G_abs', 'G_abs', 'G_abs', 'J_abs', 'H_abs', 'J_abs', 'H_abs']
bands2 = ['J_abs', 'J_abs', 'H_abs', 'K_abs', 'RP_abs', 'H_abs', 'K_abs', 'H_abs', 'K_abs']
bandsobs = ['RP_abs', 'G_abs', 'G_abs', 'G_abs', 'G_abs', 'J_abs', 'H_abs']
colors = ['RP-J', 'G-J', 'G-H', 'G-K', 'G-RP', 'J-H', 'H-K', 'J-H', 'H-K']
norm_BTSettl = plt.Normalize(vmin=np.log10(0.004*1e9), vmax=np.log10(4*1e9))
cmap_BTSettl = cm.ScalarMappable(norm=norm_BTSettl, cmap='Reds')
norm_SPOTS = plt.Normalize(vmin=np.log10(0.004*1e9), vmax=np.log10(4*1e9))
cmap_SPOTS = cm.ScalarMappable(norm=norm_SPOTS, cmap='Blues')
ages_BTSettl = np.array(list(BTSettl_Li_isochrones_Teff_dic.keys()))
ages_SPOTS = np.array(list(SPOTS_expanded_00.keys()))
for i, ax in enumerate(axs.flat):
band1 = bands1[i]
band2 = bands2[i]
bandobs = bandsobs[i]
color = colors[i]
ax.scatter(data_obs_Pleiades[band1]-data_obs_Pleiades[band2], data_obs_Pleiades[bandobs], s=10, zorder=6, color='orange', alpha=0.125)
m = bandobs.split("_")[0]
ax.set_ylabel(f'{m} [mag]')
ax.set_xlabel(f'{color} [mag]')
for age_BTSettl in BTSettl_Li_isochrones_Teff_dic.keys():
log_age_BTSettl = np.log10(age_BTSettl * 1e9)
color_BTSettl = cmap_BTSettl.to_rgba(log_age_BTSettl)
ax.plot(BTSettl_Li_isochrones_Teff_dic[age_BTSettl][band1] - BTSettl_Li_isochrones_Teff_dic[age_BTSettl][band2], BTSettl_Li_isochrones_Teff_dic[age_BTSettl][bandobs], c=color_BTSettl, lw=1, alpha=1, label='Low mass BT-Settl' if i == 0 and age_BTSettl == ages_BTSettl[-1] else "")
for age_SPOTS in SPOTS_expanded_00.keys():
log_age_SPOTS = np.log10(age_SPOTS * 1e9)
color_SPOTS = cmap_SPOTS.to_rgba(log_age_SPOTS)
ax.plot(SPOTS_expanded_00[age_SPOTS][color], SPOTS_expanded_00[age_SPOTS][bandobs], lw=1, color=color_SPOTS, zorder=1, alpha=1, label='SPOTS extended' if i == 0 and age_SPOTS == ages_SPOTS[-1] else "")
ax.invert_yaxis()
ax.plot(BTSettl_Li_isochrones[0.120][band1] - BTSettl_Li_isochrones[0.120][band2], BTSettl_Li_isochrones[0.120][bandobs], lw=1, c='k', ls='--', alpha=1, label='Low mass BT-Settl 120 Myr' if i == 0 else "")
ax.plot(SPOTS_expanded_00[0.126][color], SPOTS_expanded_00[0.126][bandobs], lw=1, color='k', ls='-', zorder=2, alpha=1, label='SPOTS extended 120 Myr' if i == 0 else "")
fig.colorbar(cmap_BTSettl, ax=axs[:, 2], orientation='vertical', label=r'$\log({\rm Age\ [yr]})$')
cbar = fig.colorbar(cmap_SPOTS, ax=axs[:, 2], orientation='vertical', label=r'$\log({\rm Age\ [yr]})$')
cbar.ax.set_yticklabels([])
cbar.ax.set_ylabel("")
fig.legend(loc='upper center', bbox_to_anchor=(0.5, 1.15))
plt.show()
plt.rcParams.update({'font.size': 11, 'axes.linewidth': 1, 'axes.edgecolor': 'k'})
plt.rcParams['font.family'] = 'serif'
SPOTS_expanded_00.keys()
dict_keys([0.004, 0.005, 0.006, 0.007, 0.008, 0.009, 0.01, 0.02, 0.028, 0.04, 0.05, 0.063, 0.071, 0.079, 0.089, 0.1, 0.126, 0.158, 0.2, 0.316, 0.398, 0.501, 0.631, 0.708, 0.794])
SPOTS_expanded_00[0.126]
| logAge | Mass | Fspot | Xspot | log(L/Lsun) | log(R/Rsun) | log(g) | log(Teff) | log(T_hot) | log(T_cool) | ... | H-K | G-H | G-K | G-V | A(Li) | Lsun | Teff | M_bol | BCv | RP-J | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 8.100000 | 1.300 | 0.0 | 0.8 | 0.412322 | 0.101930 | 4.347365 | 3.813743 | 3.813743 | 0.0 | ... | 0.03369 | 0.94187 | 0.97556 | -0.15675 | 3.246089 | 2.584178 | 6512.426278 | 3.721184 | NaN | 0.36903 |
| 1 | 8.100000 | 1.250 | 0.0 | 0.8 | 0.330458 | 0.079603 | 4.374986 | 3.804440 | 3.804440 | 0.0 | ... | 0.03575 | 1.00924 | 1.04499 | -0.16294 | 3.226090 | 2.140217 | 6374.414055 | 3.925846 | NaN | 0.39482 |
| 2 | 8.100000 | 1.200 | 0.0 | 0.8 | 0.244443 | 0.055355 | 4.405754 | 3.795061 | 3.795061 | 0.0 | ... | 0.03804 | 1.07753 | 1.11557 | -0.16850 | 3.199054 | 1.755672 | 6238.221627 | 4.140882 | NaN | 0.42129 |
| 3 | 8.100000 | 1.150 | 0.0 | 0.8 | 0.154305 | 0.029990 | 4.437999 | 3.785208 | 3.785208 | 0.0 | ... | 0.04084 | 1.14885 | 1.18969 | -0.17206 | 3.162471 | 1.426608 | 6098.292624 | 4.366229 | NaN | 0.44928 |
| 4 | 8.100000 | 1.100 | 0.0 | 0.8 | 0.060162 | 0.003835 | 4.471005 | 3.774750 | 3.774750 | 0.0 | ... | 0.04458 | 1.22739 | 1.27197 | -0.17662 | 3.113608 | 1.148582 | 5953.197490 | 4.601585 | NaN | 0.48050 |
| 5 | 8.100000 | 1.050 | 0.0 | 0.8 | -0.037916 | -0.022712 | 4.503896 | 3.763504 | 3.763504 | 0.0 | ... | 0.04858 | 1.31496 | 1.36354 | -0.20177 | 3.047699 | 0.916397 | 5801.018360 | 4.846781 | NaN | 0.51578 |
| 6 | 8.100000 | 1.000 | 0.0 | 0.8 | -0.140140 | -0.048660 | 4.534602 | 3.750922 | 3.750922 | 0.0 | ... | 0.05284 | 1.40895 | 1.46179 | -0.22926 | 2.958403 | 0.724203 | 5635.369148 | 5.102339 | NaN | 0.55408 |
| 7 | 8.100000 | 0.950 | 0.0 | 0.8 | -0.246858 | -0.073722 | 4.562450 | 3.736774 | 3.736774 | 0.0 | ... | 0.05927 | 1.52179 | 1.58106 | -0.24622 | 2.831645 | 0.566424 | 5454.735418 | 5.369136 | NaN | 0.60046 |
| 8 | 8.100000 | 0.900 | 0.0 | 0.8 | -0.358181 | -0.098047 | 4.587620 | 3.721106 | 3.721106 | 0.0 | ... | 0.06548 | 1.65187 | 1.71735 | -0.26414 | 2.641553 | 0.438348 | 5261.455359 | 5.647442 | NaN | 0.65401 |
| 9 | 8.100000 | 0.850 | 0.0 | 0.8 | -0.474711 | -0.121780 | 4.610261 | 3.703839 | 3.703839 | 0.0 | ... | 0.07325 | 1.80349 | 1.87674 | -0.30101 | 2.349644 | 0.335189 | 5056.377615 | 5.938767 | NaN | 0.71601 |
| 10 | 8.100000 | 0.800 | 0.0 | 0.8 | -0.597804 | -0.145327 | 4.631026 | 3.684840 | 3.684840 | 0.0 | ... | 0.08276 | 1.97991 | 2.06267 | -0.34286 | 1.881039 | 0.252462 | 4839.938464 | 6.246499 | NaN | 0.78762 |
| 11 | 8.100000 | 0.750 | 0.0 | 0.8 | -0.728511 | -0.169368 | 4.651080 | 3.664184 | 3.664184 | 0.0 | ... | 0.09376 | 2.19008 | 2.28384 | -0.40592 | 1.075974 | 0.186848 | 4615.127249 | 6.573268 | NaN | 0.87372 |
| 12 | 8.100000 | 0.700 | 0.0 | 0.8 | -0.864977 | -0.195221 | 4.672823 | 3.642994 | 3.642994 | 0.0 | ... | 0.11210 | 2.40327 | 2.51537 | -0.46759 | -0.421246 | 0.136466 | 4395.352697 | 6.914432 | NaN | 0.96956 |
| 13 | 8.100000 | 0.650 | 0.0 | 0.8 | -1.012628 | -0.226149 | 4.702494 | 3.621545 | 3.621545 | 0.0 | ... | 0.14166 | 2.57682 | 2.71848 | -0.55648 | -inf | 0.097134 | 4183.549674 | 7.283560 | NaN | 1.06201 |
| 14 | 8.100000 | 0.600 | 0.0 | 0.8 | -1.152988 | -0.260413 | 4.736260 | 3.603587 | 3.603587 | 0.0 | ... | 0.16852 | 2.69514 | 2.86366 | -0.61492 | -inf | 0.070309 | 4014.089117 | 7.634459 | NaN | 1.13691 |
| 15 | 8.100000 | 0.550 | 0.0 | 0.8 | -1.268473 | -0.291167 | 4.759979 | 3.590093 | 3.590093 | 0.0 | ... | 0.18849 | 2.78150 | 2.96999 | -0.68095 | -inf | 0.053892 | 3891.281506 | 7.923174 | NaN | 1.19831 |
| 16 | 8.100000 | 0.500 | 0.0 | 0.8 | -1.414063 | -0.329963 | 4.796178 | 3.573093 | 3.573093 | 0.0 | ... | 0.20717 | 2.90841 | 3.11558 | -0.81893 | -inf | 0.038542 | 3741.906660 | 8.287148 | NaN | 1.29024 |
| 17 | 8.100000 | 0.450 | 0.0 | 0.8 | -1.560148 | -0.373196 | 4.836887 | 3.558189 | 3.558189 | 0.0 | ... | 0.21722 | 3.03013 | 3.24735 | -0.92722 | -inf | 0.027533 | 3615.667953 | 8.652360 | NaN | 1.37750 |
| 18 | 8.100000 | 0.400 | 0.0 | 0.8 | -1.690418 | -0.414652 | 4.868646 | 3.546349 | 3.546349 | 0.0 | ... | 0.22274 | 3.12717 | 3.34991 | -1.00765 | -inf | 0.020398 | 3518.427448 | 8.978036 | NaN | 1.44727 |
| 19 | 8.100000 | 0.350 | 0.0 | 0.8 | -1.810935 | -0.454479 | 4.890308 | 3.536133 | 3.536133 | 0.0 | ... | 0.22728 | 3.21378 | 3.44106 | -1.10319 | -inf | 0.015455 | 3436.631053 | 9.279328 | NaN | 1.50979 |
| 20 | 8.100000 | 0.300 | 0.0 | 0.8 | -1.925703 | -0.496960 | 4.908324 | 3.528682 | 3.528682 | 0.0 | ... | 0.23084 | 3.27957 | 3.51041 | -1.18325 | -inf | 0.011866 | 3378.171160 | 9.566247 | NaN | 1.55754 |
| 21 | 8.100000 | 0.250 | 0.0 | 0.8 | -2.050513 | -0.543744 | 4.922710 | 3.520871 | 3.520871 | 0.0 | ... | 0.23494 | 3.35311 | 3.58805 | -1.28022 | -inf | 0.008902 | 3317.959343 | 9.878273 | NaN | 1.61108 |
| 22 | 8.100000 | 0.200 | 0.0 | 0.8 | -2.201137 | -0.599432 | 4.937177 | 3.511059 | 3.511059 | 0.0 | ... | 0.24098 | 3.45150 | 3.69248 | -1.43388 | -inf | 0.006293 | 3243.838470 | 10.254833 | NaN | 1.68260 |
| 23 | 8.100000 | 0.150 | 0.0 | 0.8 | -2.395952 | -0.669096 | 4.951565 | 3.497187 | 3.497187 | 0.0 | ... | 0.25040 | 3.60048 | 3.85088 | -1.73221 | -inf | 0.004018 | 3141.861887 | 10.741871 | NaN | 1.79121 |
| 24 | 8.100000 | 0.100 | 0.0 | 0.8 | -2.689492 | -0.761060 | 4.959401 | 3.469784 | 3.469784 | 0.0 | ... | 0.26728 | 3.95060 | 4.21788 | -2.47892 | -inf | 0.002044 | 2949.742462 | 11.475720 | NaN | 2.05097 |
| 25 | 8.100371 | 0.095 | 0.0 | 0.8 | -2.790000 | -0.806875 | 5.028756 | 3.467000 | 3.467000 | 0.0 | ... | 0.32900 | 4.21800 | 4.55000 | -1.95000 | -0.700000 | 0.001622 | 2930.893245 | 6.772189 | -3.58 | 2.25000 |
| 26 | 8.100371 | 0.090 | 0.0 | 0.8 | -2.980000 | -0.863279 | 5.118083 | 3.449000 | 3.449000 | 0.0 | ... | 0.35200 | 4.37500 | 4.73000 | -2.37000 | 2.345323 | 0.001047 | 2811.900830 | 6.913199 | -4.13 | 2.34000 |
| 27 | 8.100371 | 0.085 | 0.0 | 0.8 | -3.100000 | -0.899629 | 5.165960 | 3.438000 | 3.438000 | 0.0 | ... | 0.36000 | 4.53900 | 4.90000 | -2.70000 | 2.687390 | 0.000794 | 2741.574172 | 7.004074 | -4.62 | 2.45000 |
| 29 | 8.100371 | 0.080 | 0.0 | 0.8 | -3.240000 | -0.935542 | 5.211456 | 3.420000 | 3.420000 | 0.0 | ... | 0.42200 | 4.78000 | 5.20000 | -3.15000 | 3.254725 | 0.000575 | 2630.267992 | 7.093855 | -5.32 | 2.58000 |
| 31 | 8.100371 | 0.077 | 0.0 | 0.8 | -3.470000 | -0.982967 | 5.289706 | 3.384000 | 3.384000 | 0.0 | ... | 0.46500 | 5.34000 | 5.81000 | -3.09000 | 3.298695 | 0.000339 | 2421.029047 | 7.212417 | -5.78 | 3.06000 |
| 34 | 8.100371 | 0.074 | 0.0 | 0.8 | -3.600000 | -0.991400 | 5.289313 | 3.356000 | 3.356000 | 0.0 | ... | 0.51000 | 5.55000 | 6.00000 | -3.45000 | 3.300000 | 0.000251 | 2269.864852 | 7.233500 | -6.25 | 3.11000 |
| 38 | 8.100371 | 0.068 | 0.0 | 0.8 | -4.010000 | -1.026872 | 5.323535 | 3.272000 | 3.272000 | 0.0 | ... | 0.63000 | 10.21000 | 10.80000 | 0.00000 | 3.300000 | 0.000098 | 1870.682140 | 7.322180 | -7.53 | 7.47000 |
32 rows × 63 columns
SPOTS_expanded['34'][0.126]
| logAge | Mass | Fspot | Xspot | log(L/Lsun) | log(R/Rsun) | log(g) | log(Teff) | log(T_hot) | log(T_cool) | ... | H-K | G-H | G-K | G-V | A(Li) | Lsun | Teff | M_bol | BCv | RP-J | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 8.100000 | 1.300 | 0.339 | 0.8 | 0.412254 | 0.125899 | 4.299428 | 3.801741 | 3.825969 | 3.729059 | ... | 0.053157 | 1.309567 | 1.362724 | -0.139495 | 3.276616 | 2.583771 | 6334.923139 | 3.721355 | NaN | 0.518566 |
| 1 | 8.100000 | 1.250 | 0.339 | 0.8 | 0.330256 | 0.105689 | 4.322815 | 3.791347 | 3.815575 | 3.718665 | ... | 0.056602 | 1.389840 | 1.446442 | -0.141702 | 3.267539 | 2.139224 | 6185.104762 | 3.926349 | NaN | 0.550429 |
| 2 | 8.100000 | 1.200 | 0.339 | 0.8 | 0.243977 | 0.082324 | 4.351816 | 3.781460 | 3.805687 | 3.708777 | ... | 0.060524 | 1.468213 | 1.528737 | -0.142472 | 3.255346 | 1.753789 | 6045.883439 | 4.142047 | NaN | 0.581605 |
| 3 | 8.100000 | 1.150 | 0.339 | 0.8 | 0.153256 | 0.056658 | 4.384665 | 3.771612 | 3.795840 | 3.698930 | ... | 0.063878 | 1.548645 | 1.612523 | -0.141929 | 3.239359 | 1.423167 | 5910.339933 | 4.368851 | NaN | 0.613361 |
| 4 | 8.100000 | 1.100 | 0.339 | 0.8 | 0.058102 | 0.029655 | 4.419364 | 3.761325 | 3.785553 | 3.688643 | ... | 0.068538 | 1.633607 | 1.702145 | -0.137937 | 3.219617 | 1.143146 | 5771.983488 | 4.606736 | NaN | 0.647096 |
| 5 | 8.100000 | 1.050 | 0.339 | 0.8 | -0.041363 | 0.001673 | 4.455125 | 3.750450 | 3.774677 | 3.677767 | ... | 0.073601 | 1.725300 | 1.798901 | -0.133883 | 3.194028 | 0.909154 | 5629.242268 | 4.855397 | NaN | 0.683571 |
| 6 | 8.100000 | 1.000 | 0.339 | 0.8 | -0.144951 | -0.026781 | 4.490844 | 3.738780 | 3.763008 | 3.666098 | ... | 0.078869 | 1.831826 | 1.910695 | -0.147280 | 3.159421 | 0.716224 | 5479.993836 | 5.114368 | NaN | 0.726261 |
| 7 | 8.100000 | 0.950 | 0.339 | 0.8 | -0.252749 | -0.054619 | 4.524244 | 3.725750 | 3.749977 | 3.653067 | ... | 0.086146 | 1.949262 | 2.035408 | -0.159072 | 3.110233 | 0.558793 | 5318.017351 | 5.383863 | NaN | 0.775437 |
| 8 | 8.100000 | 0.900 | 0.339 | 0.8 | -0.364868 | -0.081453 | 4.554431 | 3.711137 | 3.735364 | 3.638454 | ... | 0.097240 | 2.076463 | 2.173704 | -0.159854 | 3.039105 | 0.431650 | 5142.056686 | 5.664161 | NaN | 0.833022 |
| 9 | 8.100000 | 0.850 | 0.339 | 0.8 | -0.481992 | -0.107274 | 4.581249 | 3.694766 | 3.718994 | 3.622084 | ... | 0.114929 | 2.201761 | 2.316690 | -0.163635 | 2.929287 | 0.329615 | 4951.834257 | 5.956971 | NaN | 0.896213 |
| 10 | 8.100000 | 0.800 | 0.339 | 0.8 | -0.605662 | -0.132469 | 4.605310 | 3.676446 | 3.700674 | 3.603764 | ... | 0.134222 | 2.333238 | 2.467459 | -0.192590 | 2.756017 | 0.247935 | 4747.295001 | 6.266145 | NaN | 0.966511 |
| 11 | 8.100000 | 0.750 | 0.339 | 0.8 | -0.736951 | -0.157424 | 4.627192 | 3.656102 | 3.680329 | 3.583419 | ... | 0.155998 | 2.480021 | 2.636019 | -0.230109 | 2.463906 | 0.183252 | 4530.035861 | 6.594368 | NaN | 1.052385 |
| 12 | 8.100000 | 0.700 | 0.339 | 0.8 | -0.876848 | -0.181250 | 4.644880 | 3.633040 | 3.657268 | 3.560358 | ... | 0.172535 | 2.676434 | 2.848968 | -0.299951 | 1.935785 | 0.132786 | 4295.761882 | 6.944110 | NaN | 1.168863 |
| 13 | 8.100000 | 0.650 | 0.339 | 0.8 | -1.031144 | -0.210744 | 4.671684 | 3.609213 | 3.633441 | 3.536531 | ... | 0.188190 | 2.878926 | 3.067116 | -0.360417 | 0.878639 | 0.093080 | 4066.430555 | 7.329850 | NaN | 1.295745 |
| 14 | 8.100000 | 0.600 | 0.339 | 0.8 | -1.153239 | -0.239203 | 4.693838 | 3.592919 | 3.617146 | 3.520236 | ... | 0.201647 | 3.007821 | 3.209468 | -0.431123 | -1.097940 | 0.070269 | 3916.686310 | 7.635088 | NaN | 1.384030 |
| 15 | 8.100000 | 0.550 | 0.339 | 0.8 | -1.270122 | -0.264206 | 4.706057 | 3.576200 | 3.600427 | 3.503517 | ... | 0.218399 | 3.138821 | 3.357220 | -0.474141 | -inf | 0.053688 | 3768.772046 | 7.927295 | NaN | 1.479751 |
| 16 | 8.100000 | 0.500 | 0.339 | 0.8 | -1.440761 | -0.304085 | 4.744422 | 3.553480 | 3.577707 | 3.480797 | ... | 0.240962 | 3.339630 | 3.580592 | -0.606917 | -inf | 0.036244 | 3576.675831 | 8.353893 | NaN | 1.632056 |
| 17 | 8.100000 | 0.450 | 0.339 | 0.8 | -1.598062 | -0.348402 | 4.787298 | 3.536313 | 3.560540 | 3.463630 | ... | 0.253016 | 3.511515 | 3.764531 | -0.716838 | -inf | 0.025231 | 3438.053813 | 8.747145 | NaN | 1.762608 |
| 18 | 8.100000 | 0.400 | 0.339 | 0.8 | -1.732767 | -0.389883 | 4.819107 | 3.523377 | 3.547604 | 3.450694 | ... | 0.259253 | 3.647991 | 3.907245 | -0.775352 | -inf | 0.018503 | 3337.158468 | 9.083908 | NaN | 1.867995 |
| 19 | 8.100000 | 0.350 | 0.339 | 0.8 | -1.855306 | -0.429144 | 4.839638 | 3.512373 | 3.536600 | 3.439690 | ... | 0.263619 | 3.765830 | 4.029448 | -0.853239 | -inf | 0.013954 | 3253.664509 | 9.390254 | NaN | 1.960479 |
| 20 | 8.100000 | 0.300 | 0.339 | 0.8 | -1.970007 | -0.470971 | 4.856346 | 3.504611 | 3.528839 | 3.431929 | ... | 0.266240 | 3.850802 | 4.117042 | -0.921175 | -inf | 0.010715 | 3196.032449 | 9.677009 | NaN | 2.027444 |
| 21 | 8.100000 | 0.250 | 0.339 | 0.8 | -2.093052 | -0.516734 | 4.868690 | 3.496731 | 3.520959 | 3.424049 | ... | 0.268085 | 3.943995 | 4.212080 | -1.002123 | -inf | 0.008071 | 3138.565164 | 9.984621 | NaN | 2.101488 |
| 22 | 8.100000 | 0.200 | 0.339 | 0.8 | -2.239438 | -0.570778 | 4.879868 | 3.487157 | 3.511384 | 3.414474 | ... | 0.270133 | 4.045065 | 4.315199 | -1.144710 | -inf | 0.005762 | 3070.130315 | 10.350584 | NaN | 2.179020 |
| 23 | 8.100000 | 0.150 | 0.339 | 0.8 | -2.429194 | -0.639035 | 4.891443 | 3.473846 | 3.498074 | 3.401164 | ... | 0.273108 | 4.146379 | 4.419486 | -1.452944 | -inf | 0.003722 | 2977.461880 | 10.824976 | NaN | 2.248586 |
| 24 | 8.100000 | 0.100 | 0.339 | 0.8 | -2.712418 | -0.730690 | 4.898662 | 3.448868 | 3.473095 | 3.376185 | ... | 0.279062 | 4.334799 | 4.613860 | -2.225280 | -inf | 0.001939 | 2811.043719 | 11.533036 | NaN | 2.375223 |
| 26 | 8.100371 | 0.090 | 0.340 | 0.8 | -2.980000 | -0.863279 | 5.118083 | 3.449000 | 3.449000 | 2.759200 | ... | 0.352000 | 4.375000 | 4.730000 | -2.370000 | -0.700000 | 0.001047 | 2811.900830 | 6.913199 | -4.13 | 2.340000 |
| 27 | 8.100371 | 0.085 | 0.340 | 0.8 | -3.100000 | -0.899629 | 5.165960 | 3.438000 | 3.438000 | 2.750400 | ... | 0.360000 | 4.539000 | 4.900000 | -2.700000 | 2.687390 | 0.000794 | 2741.574172 | 7.004074 | -4.62 | 2.450000 |
| 29 | 8.100371 | 0.080 | 0.340 | 0.8 | -3.240000 | -0.935542 | 5.211456 | 3.420000 | 3.420000 | 2.736000 | ... | 0.422000 | 4.780000 | 5.200000 | -3.150000 | 3.254725 | 0.000575 | 2630.267992 | 7.093855 | -5.32 | 2.580000 |
| 31 | 8.100371 | 0.077 | 0.340 | 0.8 | -3.470000 | -0.982967 | 5.289706 | 3.384000 | 3.384000 | 2.707200 | ... | 0.465000 | 5.340000 | 5.810000 | -3.090000 | 3.298695 | 0.000339 | 2421.029047 | 7.212417 | -5.78 | 3.060000 |
| 34 | 8.100371 | 0.074 | 0.340 | 0.8 | -3.600000 | -0.991400 | 5.289313 | 3.356000 | 3.356000 | 2.684800 | ... | 0.510000 | 5.550000 | 6.000000 | -3.450000 | 3.300000 | 0.000251 | 2269.864852 | 7.233500 | -6.25 | 3.110000 |
| 38 | 8.100371 | 0.068 | 0.340 | 0.8 | -4.010000 | -1.026872 | 5.323535 | 3.272000 | 3.272000 | 2.617600 | ... | 0.630000 | 10.210000 | 10.800000 | 0.000000 | 3.300000 | 0.000098 | 1870.682140 | 7.322180 | -7.53 | 7.470000 |
31 rows × 63 columns
SPOTS_edr3_full['00'].keys()
dict_keys([0.004, 0.005, 0.006, 0.007, 0.008, 0.009, 0.01, 0.02, 0.028, 0.04, 0.05, 0.063, 0.071, 0.079, 0.089, 0.1, 0.126, 0.158, 0.2, 0.316, 0.398, 0.501, 0.631, 0.708, 0.794, 0.891, 1.0, 1.995, 3.162, 3.981])
Isochrones plots¶
plt.rcParams.update({'font.size': 14, 'axes.linewidth': 1, 'axes.edgecolor': 'k'})
plt.rcParams['font.family'] = 'serif'
Isochrones in HRD (interior) and CMD (atmosphere)
from models_test import Isochrones
bands1 = [['G_abs', 'RP_abs'], ['G_abs', 'J_abs'], ['J_abs', 'K_abs'], ['G_abs', 'y_abs'], ['G_abs', 'z_abs']]
bands2 = [['G_i00', 'G_RP_i00'], ['G_i00', 'J_i00'], ['J_i00', 'Ks_i00'], ['G_i00', 'yP1_i00'], ['G_i00', 'zP1_i00']]
bandsobs = [['G', 'RP'], ['G', 'J'], ['J', 'K'], ['G', 'y'], ['G', 'z']]
isochrones = [0.02, 0.08, 0.12, 0.6]
isochrones_grid = Isochrones(
BTSettl_Li_isochrones,
PARSEC_iso_omega_00_Phot_dict,
bands1,
bands2,
'BT-Settl $\odot$',
r'PARSEC $\omega_i=0.0$; $Z=0.001547$',
isochrones,
filename='model_comparison',
data_obs=data_obs_Pleiades,
obs=True,
bandsobs=bandsobs,
dpi=350
)
%matplotlib inline
isochrones_grid.plot_isochrones_grid()
Saved figure as model_comparison_BT-Settl_PARSEC.pdf
%matplotlib inline
isochrones_grid.plot_single_column(['BP_abs', 'RP_abs', 'G_abs'], ['G_BP_i00', 'G_RP_i00', 'G_i00'], ['BP', 'RP', 'G'])
Saved figure as model_comparison_BT-Settl_PARSEC.pdf